Sub-Terahertz Sensing Technology for Biomedical Applications (Biological and Medical Physics, Biomedical Engineering) 9811931399, 9789811931390

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Biological and Medical Physics, Biomedical Engineering

Shiban Kishen Koul Priyansha Kaurav

Sub-Terahertz Sensing Technology for Biomedical Applications

Biological and Medical Physics, Biomedical Engineering Editor-in-Chief Bernard S. Gerstman, Department of Physics, Florida International University, Miami, FL, USA Series Editors Masuo Aizawa, Tokyo Institute Technology, Tokyo, Japan Robert H. Austin, Princeton, NJ, USA James Barber, Wolfson Laboratories, Imperial College of Science Technology, London, UK Howard C. Berg, Cambridge, MA, USA Robert Callender, Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY, USA George Feher, Department of Physics, University of California, San Diego, La Jolla, CA, USA Hans Frauenfelder, Los Alamos, NM, USA Ivar Giaever, Rensselaer Polytechnic Institute, Troy, NY, USA Pierre Joliot, Institute de Biologie Physico-Chimique, Fondation Edmond de Rothschild, Paris, France Lajos Keszthelyi, Biological Research Center, Hungarian Academy of Sciences, Szeged, Hungary Paul W. King, Biosciences Center and Photobiology, National Renewable Energy Laboratory, Lakewood, CO, USA Gianluca Lazzi, University of Utah, Salt Lake City, UT, USA Aaron Lewis, Department of Applied Physics, Hebrew University, Jerusalem, Israel Stuart M. Lindsay, Department of Physics and Astronomy, Arizona State University, Tempe, AZ, USA Xiang Yang Liu, Department of Physics, Faculty of Sciences, National University of Singapore, Singapore, Singapore David Mauzerall, Rockefeller University, New York, NY, USA Eugenie V. Mielczarek, Department of Physics and Astronomy, George Mason University, Fairfax, USA

Markolf Niemz, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany V. Adrian Parsegian, Physical Science Laboratory, National Institutes of Health, Bethesda, MD, USA Linda S. Powers, University of Arizona, Tucson, AZ, USA Earl W. Prohofsky, Department of Physics, Purdue University, West Lafayette, IN, USA Tatiana K. Rostovtseva, NICHD, National Institutes of Health, Bethesda, MD, USA Andrew Rubin, Department of Biophysics, Moscow State University, Moscow, Russia Michael Seibert, National Renewable Energy Laboratory, Golden, CO, USA Nongjian Tao, Biodesign Center for Bioelectronics, Arizona State University, Tempe, AZ, USA David Thomas, Department of Biochemistry, University of Minnesota Medical School, Minneapolis, MN, USA

This series is intended to be comprehensive, covering a broad range of topics important to the study of the physical, chemical and biological sciences. Its goal is to provide scientists and engineers with textbooks, monographs, and reference works to address the growing need for information. The fields of biological and medical physics and biomedical engineering are broad, multidisciplinary and dynamic. They lie at the crossroads of frontier research in physics, biology, chemistry, and medicine. Books in the series emphasize established and emergent areas of science including molecular, membrane, and mathematical biophysics; photosynthetic energy harvesting and conversion; information processing; physical principles of genetics; sensory communications; automata networks, neural networks, and cellular automata. Equally important is coverage of applied aspects of biological and medical physics and biomedical engineering such as molecular electronic components and devices, biosensors, medicine, imaging, physical principles of renewable energy production, advanced prostheses, and environmental control and engineering.

Shiban Kishen Koul · Priyansha Kaurav

Sub-Terahertz Sensing Technology for Biomedical Applications

Shiban Kishen Koul Center for Applied Research in Electronics Indian Institute of Technology Delhi New Delhi, India

Priyansha Kaurav Institute for High Frequency & Communication Technology University of Wuppertal Wuppertal, Germany

ISSN 1618-7210 ISSN 2197-5647 (electronic) Biological and Medical Physics, Biomedical Engineering ISBN 978-981-19-3139-0 ISBN 978-981-19-3140-6 (eBook) https://doi.org/10.1007/978-981-19-3140-6 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

We would like to dedicate our efforts to our wonderful families who inspired and supported us through everything.

Preface

The modern-day terahertz (THz) spectrometers and imaging systems have become standard laboratory tools thanks to advances in THz source and detector technology. The sub-THz band covers the frequency spectrum of 0.1–0.3 THz, and the THz extends from 0.3 to 10 THz frequency range. Their applications range from explosive and concealed weapon detection, non-destructive testing, pharmaceutical drug quality control, and biomedical imaging. The concept of using sub-THz and THz radiations for ex-vivo and in-vivo tissue diagnostic and screening purposes has attracted enormous interest due to their high sensitivity and non-ionizing properties. The ability to provide a broad spectrum of spectral responses has led to an increase in the popularity of time-domain spectrometers (TDS), which use electro-optical pulses to analyze biological samples. Because these electro-optic sampling-based TDS setups are bulky and expensive, it is not economically feasible to use this technology for the purpose of developing portable and inexpensive imaging and sensing systems in the THz region of the Electromagnetics (EM). In addition, because of the lower efficiency of the laser source, these setups have poor accuracy in the subTHz frequency range as a result. It is now possible to develop compact, robust, and simple-to-use systems for biomedical applications thanks to advances in electronic technology at sub-THz frequencies. Furthermore, several biomedical applications of the electromagnetic spectrum necessitate the real-time monitoring of dynamic parameters of physiological states in humans. When it comes to these applications, computation-intensive methods utilizing massive parallelization techniques on high-performance platforms such as GPUs/TPUs are required for the analysis of biomedical signals. These computing systems have made it possible to model large amounts of data extremely effectively using machine learning. Sub-THz technology is combined with machine learning in this book to produce an automated diagnostic approach for extracting and analyzing information from electronic sub-THz measurement systems. The concepts of artificial intelligence (AI)-enabled sub-THz systems for novel applications in the biomedical field are introduced in this book, providing readers with an opportunity to learn about them. Furthermore, the readers will be motivated

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to contextualize these applications to solve real-world problems such as non-invasive glucose monitoring systems, cancer detection systems, and dental imaging systems. Chapter 1 of the book discusses how the THz frequency band has evolved in the biomedical domain. It emphasizes its advantages over other frequency regimes such as microwaves and infrared. The section also examines various ways in which THz diagnostics, imaging, and treatment can contribute to biomedical research and treatment. The second section of this chapter compares electronics-based THz technology with other conventional electro-optic THz setups, with the results highlighting the superior efficiency, affordability, and portability of electronics-based THz systems over the other options, especially in sub-THz region. In the remaining chapters, sub-THz, and THz measurement systems for a variety of biomedical applications are discussed. Specifically, the chapters describe three major applications in which sub-THz provides an advantage over the current stateof-the-art technologies. These applications include non-invasive measurement of blood glucose levels, intraoperative assessment of tumor margins, and skin cancer detection, among others. Thorough description of application of machine learning for measurement systems for non-invasive glucose concentration measurement is presented in detail. This makes it easier for the reader to relate to the output in a more user-friendly format and to comprehend the various possible use cases in a clear and concise manner. Furthermore, the book assists the reader in learning how to construct tissue phantoms and characterize them at sub-THz frequencies to test the measurement systems. A brief introduction to system automation for biomedical imaging is provided at the end of the book, which will help the reader perform a quick analysis of the data. We believe that the book will empower the reader to understand and appreciate the immense possibilities of using sub-THz systems in the biomedical field, creating gateways for fueling further research in this area. New Delhi, India Wuppertal, Germany

Shiban Kishen Koul Priyansha Kaurav

Contents

1 Terahertz Spectrum in Biomedical Engineering . . . . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Terahertz Biomedical Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Terahertz in Medical Diagnostics . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Terahertz in Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Terahertz in Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Terahertz Instrumentation: From Photonics to Electronics . . . . . . . . 1.3.1 Terahertz Photonics Devices and Techniques . . . . . . . . . . . . . 1.3.2 Terahertz Electronic Devices and Techniques . . . . . . . . . . . . 1.4 Artificial Intelligence in Sub-Terahertz Bioelectronics . . . . . . . . . . . 1.5 Scope of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 4 5 7 11 12 13 17 20 22 23

2 Electronic Sub-Terahertz VNA Measurement Techniques . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Terahertz VNA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Scattering Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Generic VNA Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Terahertz VNA Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Sub-Terahertz/Terahertz Frequency Translation Using Extenders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Terahertz VNA Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Terahertz VNA Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Calibration Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Sub-Terahertz VNA Measurement Systems for Permittivity Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.1 Nicolson-Ross-Weir Technique . . . . . . . . . . . . . . . . . . . . . . . . 2.6.2 Quasi-Optical Free-Space Permittivity Measurement . . . . . . 2.6.3 Open-Waveguide Probe for Viscous Liquid Permittivity Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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3 Biological Tissue Interaction with Sub-Terahertz Wave . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Dielectric Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Dielectric Polarization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Dielectric Spectroscopy Parameter: Complex Permittivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Frequency Response of Dielectric Mechanisms . . . . . . . . . . . 3.2.4 Relaxation Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Dielectric Characterization of Water in Sub-Terahertz/Terahertz Regime . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Relaxation Models of Water . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Dielectric Characterization of Biological Solution Using Terahertz Hydration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Effective Medium Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 Maxwell Garnett Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.2 Bruggeman Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.3 Landau-Lifshitz-Looyenga Model . . . . . . . . . . . . . . . . . . . . . . 3.5.4 Polder and Van Santen Model for Ellipsoidal Particles . . . . . 3.6 Dielectric Constant of Biological Materials in Sub-Terahertz Spectrum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.1 Dielectric Spectra of Saccharide Solutions . . . . . . . . . . . . . . . 3.6.2 Dielectric Spectra of Blood . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.3 Dielectric Spectra of Protein Solutions . . . . . . . . . . . . . . . . . . 3.6.4 Dielectric Spectra of Biological Tissues . . . . . . . . . . . . . . . . . 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4 Non-invasive Sub-Terahertz Blood Glucose Measurement . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Non-ionizing Blood Glucose Measurement Techniques Using EM Waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Penetration Depth of the EM Wave with Respect to Frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.2 Performance Evaluation Parameters . . . . . . . . . . . . . . . . . . . . 4.2.3 ISO 15197: Accuracy Assessment Standard . . . . . . . . . . . . . . 4.2.4 Non-invasive Glucose Measurements Using Intrinsic Properties of Glucose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.5 Non-invasive Glucose Measurements Using Dielectric Properties of Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Sub-Terahertz Spectrum for Non-invasive Evaluation of Glucose Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Penetration Depth of Sub-Terahertz Wave Inside Blood Tissue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Dielectric Properties of Glucose with Variable Concentration in Sub-Terahertz Spectrum . . . . . . . . . . . . . . .

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4.4 Tissue Phantom Models for Glucose Concentration Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Tissue-Mimicking Phantoms . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Phantoms for Non-invasive Glucose Concentration Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Non-invasive Sub-Terahertz Glucose Concentration Measurement Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Measurement Using Reflection Properties of Sub-Terahertz Wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.2 Measurement Using Transmission Properties of Sub-Terahertz Wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Breast Tumor Margin Assessment Using Sub-Terahertz Wave . . . . . . 5.1 Breast-Conserving Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Histopathological Assessment of Excised Tissue . . . . . . . . . . 5.2 Intraoperative Tumor Margin Assessment . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Criteria for Developing Intraoperative Tumor Margin Assessment Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Current Intraoperative Techniques for Breast Tumor Margin Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Pathology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Nuclear Medicine: Positron Emission Tomography . . . . . . . 5.3.3 Electromagnetic Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 EM Interaction of Excised Breast Tissue in Sub-Terahertz/Terahertz Bands . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Breast Tissues Characterization in Sub-Terahertz/Terahertz Frequency Bands . . . . . . . . . . . . . 5.4.2 Excised Breast Tissue Phantoms . . . . . . . . . . . . . . . . . . . . . . . 5.5 Sub-Terahertz/Terahertz Imaging for Breast Tumor Margin Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Photonics-Based Terahertz Imaging System . . . . . . . . . . . . . . 5.5.2 Electronics-Based Terahertz Imaging System . . . . . . . . . . . . 5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Sub-Terahertz and Terahertz Waves for Skin Diagnosis and Therapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Overview of Skin Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Types and Stages of Melanoma . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Skin Cancer Diagnosis and Treatment . . . . . . . . . . . . . . . . . . . 6.2 Advanced Skin Cancer Diagnostic Techniques . . . . . . . . . . . . . . . . . . 6.2.1 Multispectral Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Electrical Bioimpedance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 High-Frequency Ultrasound . . . . . . . . . . . . . . . . . . . . . . . . . . .

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6.2.4 Optical Coherence Tomography . . . . . . . . . . . . . . . . . . . . . . . . 6.2.5 Confocal Microscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.6 Raman Spectroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Interaction of Sub-Terahertz/Terahertz Radiation with Human Skin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Dielectric Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 N-Layered Interaction Models of Skin . . . . . . . . . . . . . . . . . . 6.4 Sub-Terahertz/Terahertz Imaging for Skin Cancer Detection . . . . . . 6.4.1 Non-melanoma Skin Cancer Imaging . . . . . . . . . . . . . . . . . . . 6.4.2 Melanoma Skin Cancer Imaging . . . . . . . . . . . . . . . . . . . . . . . 6.5 Therapeutic Applications of Sub-Terahertz/Terahertz Radiation on the Skin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Machine Learning and Biomedical Sub-Terahertz/Terahertz Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Machine Learning in Biomedical Engineering . . . . . . . . . . . . . . . . . . 7.2 ML Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Steps in Supervised Learning Models . . . . . . . . . . . . . . . . . . . 7.3.2 Algorithms in Supervised Learning Model . . . . . . . . . . . . . . . 7.4 Unsupervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.1 Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4.2 Principal Component Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 7.5 Performance Metrics of ML Algorithms . . . . . . . . . . . . . . . . . . . . . . . 7.5.1 Regression Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . 7.5.2 Classification Performance Metrics . . . . . . . . . . . . . . . . . . . . . 7.6 Performance Evaluation Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.1 Holdout Evaluation Technique . . . . . . . . . . . . . . . . . . . . . . . . . 7.6.2 Cross-Validation Evaluation Technique . . . . . . . . . . . . . . . . . . 7.7 ML in Sub-Terahertz/Terahertz Technology . . . . . . . . . . . . . . . . . . . . 7.7.1 ML in Sub-Terahertz/Terahertz Biomedical Signal Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.7.2 ML in Sub-Terahertz/Terahertz Biomedical Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Automation in Sub-Terahertz/Terahertz Imaging Systems . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Automation in Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Motorized Stages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Robotics Arms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 MATLAB Based Terahertz VNA Automation . . . . . . . . . . . . . . . . . . . 8.4 Automation in Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

173 174 175 177 178 182 183 183 185 188 190 191 199 199 202 204 205 206 213 214 215 217 217 219 222 222 222 223 223 227 231 232 241 241 243 244 247 252 253

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8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 256 Appendix A: Modified Newton Raphson Method . . . . . . . . . . . . . . . . . . . . . 259 Appendix B: Relation Between Complex Permittivity and Complex Refractive Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 Appendix C: MATLAB Code for Establishing a Connection Between VNA and PC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 271 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273

About the Authors

Shiban Kishen Koul received the B.E. degree in electrical engineering from Regional Engineering College, Srinagar, India, in 1977, and the M.Tech. and Ph.D. degrees in microwave engineering from the Indian Institute of Technology Delhi, New Delhi, India, in 1979 and 1983, respectively. He has been an Emeritus Professor with the Indian Institute of Technology, Delhi, since 2019 and Mentor Deputy Director (Strategy and Planning, International affairs) with IIT Jammu, J&K, India, since 2018. He served as Deputy Director (Strategy and Planning) with IIT Delhi from 2012–2016. He also served as the Chairman of Astra Microwave Products Limited, Hyderabad, from 2009–2019, and Dr. R. P. Shenoy Astra Microwave Chair Professor at IIT Delhi from 2014–2019. His research interests include RF MEMS, high-frequency wireless communication, microwave engineering, microwave passive and active circuits, device modeling, millimeter and submillimeter-wave IC design, body area networks, flexible and wearable electronics, medical applications of subterahertz waves and reconfigurable microwave circuits, including miniaturized antennas. He has successfully completed 38 major sponsored projects, 52 consultancy projects, and 61 technology development projects. He has authored/co-authored 570 research papers, 18 stateof-the-art books, four book chapters, and two e-books. He holds 26 patents, six copyrights, and one trademark. He has guided 28 Ph.D. theses and more than 120 master’s theses. Professor Koul is a Life Fellow of IEEE and Fellow of INAE and IETE. He is the Chief Editor of the IETE xv

xvi

About the Authors

Journal of Research, Associate Editor of the International Journal of Microwave and Wireless Technologies, Cambridge University Press. He served as a Distinguished Microwave Lecturer of IEEE MTT-S for the period 2012–2014. Prior to this, he served as a Speaker Bureau Lecturer of IEEE MTT-S. He also served as an AdCom member of the IEEE MTT-S from 2010– 2018 and is presently a member of the Awards, Nomination, and Appointments, MGA, M&S, and Education Committees of the IEEE MTT-S. He is the recipient of numerous awards, including IEEE MTT Society Distinguished Educator Award (2014); Teaching Excellence Award (2012) from IIT Delhi; Indian National Science Academy (INSA) Young Scientist Award (1986); Top Invention Award (1991) of the National Research Development Council for his contributions to the indigenous development of ferrite phase shifter technology; VASVIK Award (1994) for the development of Ka-band components and phase shifters; Ram Lal Wadhwa Gold Medal (1995) from the Institution of Electronics and Communication Engineers (IETE); Academic Excellence Award (1998) from Indian Government for his pioneering contributions to phase control modules for Rajendra Radar, Shri Om Prakash Bhasin Award (2009) in the field of Electronics and Information Technology, VASVIK Award (2012) for the contributions made to the area of Information, Communication Technology (ICT) and M N Saha Memorial Award (2013) from IETE. Priyansha Kaurav as a Prime Minister’s Research Fellow (PMRF), completed her Ph.D. in 2022 at the Center for Applied Research in Electronics (CARE), Indian Institute of Technology (IIT) Delhi. She is currently working as a research scientist in Institute of High Frequency System and Communication Technology (IHCT) in University of Wuppertal, Germany. Priyansha’s primary research is centered around Electronics Terahertz Sensors and Artificial Intelligence in RF and Microwave. She has several highquality publications and patents under her name. Some of the key ones include “Sub-Terahertz apparatus and method for subsurface malignant tissue imaging,” Indian Patent application no.: 202111028062, Apparatus and Method for Non-invasive Measurement of

About the Authors

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Blood Glucose Concentration, Indian Patent application no.: 202011050895, A system and a method for material characterization, Indian Patent application no.: 202011008994. Her major publications include “Electromagnetic Characterization of Breast Tissue Phantoms in D-Band Regime in IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology and Non-invasive Glucose Measurement Using SubTerahertz Sensor, Time-Domain Processing and Neural Network in IEEE Sensors. She has won the Innovation Award at IIT Delhi (2016) and received Travel Support for International Cooperation Program organized by Japan International Cooperation Center (JICE). She is also the recipient of the Academic Excellence Award from IIITDM Jabalpur. Prior to joining Ph.D. Program, she worked as RF and Design Engineer at Qualcomm India. She is a reviewer for several leading transactions and journals in the fields of biomedical applications of microwave, millimeter, and terahertz sensors and systems. She is also an active member of IEEE Microwave Theory and Techniques Society and is currently holding the position of Treasurer at IEEE MTT-S Student Branch Chapter, IIT Delhi, and key Women In Engineering Representative position in the same Chapter.

Abbreviations

2D 3D AI ATR AUC-ROC BCC BCS BPNN BSA BW BWO CC-PCF CEG CGBM CLI CNN Cr-GaAs CST CT CW DAST DCIS DD DFG DL DNN DRA DUT EDC EIS EM

Two-Dimensional Three-Dimensional Artificial Intelligence Attenuated Total Internal Reflection Area under Receiver operating characteristics curve Basal Cell Carcinoma Breast-Conserving Surgery Backpropagation Neural Network Bovine Serum Albumin Bandwidth Backward-Wave Oscillators Photonic Crystal Fiber Clarke Error Grid Continuous Blood Glucose Monitoring Cherenkov luminescence imaging Convolutional Neural Network Chromium-Doped Gallium Arsenide Computer Simulation Technology Computed Tomography Continuous Wave Diethyl Amino Sulphur Trifluoride Ductal Carcinoma In Situ Double Debye Difference Frequency Generator Deep Learning Deep Neural Networks Dielectric Resonator Antenna Device under Test Epidermal Differentiation Complex Electrical Impedance Spectroscopy Electromagnetic xix

xx

EOSC FDG FDTD FFPE FN FNN FP FPR FSA GaAs GD GFRP GHz GI GN GOx GRL GUI HB HBT HDPE HEMT HFSS HFUS IDC IF IHC ILC InP ISF ISO k-NN LiNbO3 LLL LO LOD LOOCV LRL LSPA LSRR MAE MAPE MCK MG MIR

Abbreviations

Emphatic Orthogonal Signal Correction Fluorodeoxyglucose Finite Difference Time Domain Formalin-Fixed Paraffin Embedded False Negative Feed-Forward Neural Network False Positive False-Positive Rate Frozen Section Analysis Gallium Arsenide Gradient Descent Glass-Fiber-Reinforced Polymer Gigahertz Gastrointestinal Gauss-Newton Glucose Oxidase Gate-Reflect-Line Graphical User Interface Hydrogen Bond Heterojunction Bipolar Transistor High-Density Polyethylene High Electron Mobility Transistor High-Frequency Structure Simulator High-Frequency Ultrasonography Invasive Ductal Carcinoma Intermediate Frequency Immunohistochemical Invasive Lobular Carcinoma Indium Phosphide Interstitial Fluid International Standards Organization k-Nearest Neighbor Lithium Niobate Landau-Lifshitz-Looyenga Local Oscillator Limit of Detection Leave-One-Out Cross-Validation Line-Reflect-Line Linear Discriminant Analysis Localized Surface Plasmon Resonance Mean Absolute Error Mean Absolute Percentage Error Material Characterization Kit Maxwell Garnett Mid-Infrared

Abbreviations

ML MM MMIC mmW MRI MSE MW NCCN NEP NHB NIR NMSC NN NRW OCT OR PC PCA PC-OSC PET PvS QCL QO-FSM RBC RCM RD-SOS ReLU RMSE RNN ROI S11 S21 SC SCC SG SGC S-matrix SNR SOL SOLT SSE SSL SVM TBI TCS

xxi

Machine Learning Malignant Melanoma Microwave Monolithic Integrated Circuit Millimeter Wave Magnetic Resonance Imaging Mean Squared Error Microwave National Comprehensive Cancer Network Noise Equivalent Power Non-Hydrogen-Bond Near-Infrared Nonmelanoma skin cancer Neural Network Nicolson-Ross-Weir Optical Coherence Tomography Optical Rectification Principal Component Principal Component Analysis Principal Component Orthogonal Signal Correction Positron Emission Tomography Polder and van Santen Quantum Cascade Lasers Quasi-Optical Free-Space Material Red Blood Cells Reflectance Confocal Microscopy Radiation-Damaged Silicon-on-Sapphire Rectified Linear Unit Root-Mean-Square Error Recurrent Neural Networks Region of Interest Reflection Coefficient Transmission Response Stratum Corneum Squamous Cell Carcinoma Savitzky–Golay Sebaceous Gland Carcinoma Scattering matrix Signal-to-Noise Ratio Short-Open-Load Short-Open-Load-Thru Sum of Square Error Short-Short-Load Support Vector Machine Traumatic Brain Injury Tissue-Conserving Surgery

xxii

TDS THz Ti: Sapphire TL TN TP TPI TPP TPR TRL TSM US UWB VDI VISA VNA VOC WBC WHO WI WPT ZnTe

Abbreviations

Time-Domain Spectroscopy Terahertz Titanium-Doped Aluminum Oxide Transmission Line True Negative True Positive THz Pulsed Imaging Time Post-Pulse True Positive Rate Thru-Reflect-Line Thru-Short-Match Ultrasound Ultra-wideband Virginia Diodes Inc. Virtual Instrument Software Architecture Vector Network Analyzer Volatile Organic Compound White Blood Cell World Health Organization Waveguide Iris Wavelet Packet Transform Zinc Telluride

Chapter 1

Terahertz Spectrum in Biomedical Engineering

Abstract In recent years, terahertz radiation (THz = 1012 Hz) has attracted much attention due to its exceptional non-invasive and non-ionizing sensing capabilities. The sub-THz band (0.1–0.3 THz) and the THz band (0.3–10 THz) lie between millimeter waves (mm-waves) and light waves with the ability to harness their advantages. The capacity for these sub-THz and THz waves to penetrate deeply into dielectric materials combined with their high spatial resolution makes them well suited for biomedical applications, including in-vivo and ex-vivo experiments. The purpose of this chapter is to discuss how the sensors based on these frequency spectra can be used in various biomedical applications, classified into three major domains, i.e., diagnostics, imaging, and treatment, where they provide many advantages over the existing devices. Next, we will discuss the appropriateness of using photonics and electronics THz instruments in THz applications and the suitability of using electronics in the sub-THz regime. Finally, we’ll look at artificial intelligence’s function in enhancing the technology’s versatility.

1.1 Introduction Studies of electromagnetic fields’ interactions with biological systems have played a significant role in advancing biomedical technology based on electromagnetics. Researchers have used electromagnetic biomedical studies in various applications, including therapeutic diagnosis, cancer detection and imaging, healthcare informatics, wireless bio-implants, and evaluation of electromagnetic radiation health hazards. In the last few decades, progress in the Electromagnetic (EM) spectrum ranging from radio waves to gamma rays has revolutionized the medical scanning and disease diagnosis systems [1–15]. A few illustrations are shown in Fig. 1.1. Different regions of the EM spectrum interact with matter in different ways. As the frequency is increased, the interaction of EM waves progresses from the human body level in radio waves to the DNA level in X-rays and Gamma rays. High-frequency waves with an increased energy value, such as X-rays, are classified as ionizing because they create enough energy to form ions at the molecular level, inflicting © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. K. Koul and P. Kaurav, Sub-Terahertz Sensing Technology for Biomedical Applications, Biological and Medical Physics, Biomedical Engineering, https://doi.org/10.1007/978-981-19-3140-6_1

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1 Terahertz Spectrum in Biomedical Engineering

Fig. 1.1 Biomedical applications of different regions of electromagnetic spectrum

damage to DNA and proteins in the process. The lower frequency range, spanning from radio waves to visible light, is non-ionizing in nature and, as a result, does not hinder the functioning of human cells [16]. The biomedical applications are broadly classified into four groups: treatment, imaging, directed movement, and diagnostics [17]. Some of the applications in these groups are illustrated in Fig. 1.2. There are different constraints for each application to develop the relevant technology. For instance, a highly sensitive device needs to be used for diagnostic applications that can penetrate properly inside the tissue, observing the small changes in the analyte levels [18, 19]. Spatial resolution and EM wave penetration of the tissue for cancer imaging applications are required to improve instrument accuracy [20, 21]. The properties of EM waves vary with an increase in frequency. The radio waves and microwaves lying in the lower EM spectrum can penetrate deep inside the tissue due to larger wavelengths but compromise spatial resolution and sensitivity characteristics due to larger wavelengths [22]. On the other hand, moving to the higher end of the EM spectrum, the infrared and the optical rays provide high sensitivity and spatial resolution devices for disease diagnosis and imaging applications but suffer from lesser penetration depths [23]. Terahertz (THz) is a small spectrum between the microwave and the infrared waves, as shown in Fig. 1.1. At the lower frequency limit, the electromagnetically covered spectrum of the THz waves is ambiguous. According to some published research in the literature, the THz spectrum ranges from 0.3 to 10 THz [24, 25], whereas other works define the spectrum as ranging from 0.1 to 10 THz [26–28]. The THz and sub-THz bands will be used in this book to refer to the frequency bands 0.3–10 THz and 0.1–0.3 THz, respectively. However, in the next section, while discussing the biomedical applications, THz radiation, in general, will be denoted by both THz and sub-THz spectrum. The THz frequency range lies between the upper and lower physical limits of electronic and optical devices, so studies, research, and innovation have been restricted in this spectral range compared to the microwave, optical, and X-ray frequencies, which are well developed [24].

1.1 Introduction

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Fig. 1.2 Wide classification of biomedical engineering by different kinds of application

Due to THz source and detector technology advancements over the past two decades, THz waves have progressed into new frontiers in science. Consequently, these waves are now being used in a wide range of disciplines, from physics to medicine to biology to astronomy. The following characteristics of THz waves (comprising of THz and sub-THz spectrum) make them more appropriate for biomedical diagnosis and imaging applications compared to other frequency domains of the EM spectrum [25]: 1.

2. 3.

Non-Ionizing: Since THz radiation (with an energy range of 0.1–10 meV) emits low-energy photons compared to X-rays, which do not cause ionization damage, this radiation is ideal for medical imaging since it allows in-vivo real-time diagnostics without ionizing the tissue. Scattering: THz wavelengths are much longer than visible and infrared light wavelengths, which would lead to minimal scattering losses in biological tissues. Spatial Resolution: Near-field spectroscopic modalities based on spatiotemporal dynamics of THz radiation provide a spatial resolution of several micrometers and a time resolution from sub-picoseconds to picoseconds. This feature of THz spectroscopy enables time-resolved studies of the collective modes of vibrations of biomolecules in solution with unprecedented sensitivity.

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4.

5.

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Optical signature: There is a strong correlation between the THz wavelength and the energy levels corresponding to molecular motions, such as vibration, translation, and rotation. Hence, THz-frequency spectral signatures are used to identify biomolecules by measuring their characteristic motions. Material transparency of nonpolar molecules: The THz frequency range can be used to reveal material that is opaque at visible and near-infrared scales but transparent at smaller scales. As a result of this property, a hazardous substance can easily be detected without opening a package due to the ease with which THz radiation can penetrate common packaging materials. Water Absorption: Strong absorption in the THz range is seen in polar molecules, such as water. This makes it possible to distinguish cancerous tissues from normal tissues due to their distinct water content. Additionally, THz radiation can quickly and easily determine the hydration levels of microorganisms using water absorption characteristics.

1.2 Terahertz Biomedical Applications Many investigations have been carried out exploiting these unique characteristics of THz waves for the development of THz-biomedical technologies in diagnostics, imaging, and treatment subgroups, as demonstrated in Fig. 1.2. The potential applications and currently used state-of-the-art THz techniques in these domains are discussed in the following subsections that have been summarized in graphical illustration in Fig. 1.3.

Fig. 1.3 Applications of THz technology in medical diagnostics, imaging, and treatment

1.2 Terahertz Biomedical Applications

5

1.2.1 Terahertz in Medical Diagnostics Since most low-frequency biomolecular movements, such as vibration and rotation of the molecular skeleton, occur in the same frequency range as THz radiation, there has been considerable interest in using THz spectroscopy to explore and describe different biomaterials in recent decades [25]. Consequently, different biomolecules may be successfully identified and described based on their unique spectroscopic signatures, obtained using spectral fingerprint analysis. The following sub-sections discuss the applications of THz spectroscopy in the spectroscopic signature sensing, pathological examination of body fluids, and gas spectroscopy applications.

1.2.1.1

Biomolecules Spectral Signatures Sensing

THz spectroscopy offers distinct benefits over other methods. For instance, THz pulse width may examine time-resolved biological samples and efficiently reduce far-infrared background noise. In addition, THz spectra may reveal three-dimensional molecular organization and characteristic low-frequency absorption [26]. Some of the medical applications using the biomolecule spectral sensing applications of THz are listed below [26]: Nucleic acids: Methods for detecting nucleic acid molecules label-free include colorimetry, microwave resonance absorption, optical biosensors, and electrochemical. However, the advancement of these label-free technologies is not sufficient to replace fluorescent labeling. Although the resonant THz structures used as sample carriers have femtomolar sensitivity, nucleic acid hybridization may be probed with the same sensitivity as fluorescence labeling methods [27]. Also, a theoretical study and practical observations have shown that singleand double-stranded DNA exhibits distinct THz signatures at around 0.4 and 0.7 THz [28]. These properties have been used to detect an aqueous DNA molecule utilizing waveguide confinement methods, partly eliminating the water absorption phenomenon that presently restricts THz technology [29]. Amino Acids, Peptides, and Carbohydrates: In-vitro diagnostic medical devices made possible by THz technology include those that screen for illness in blood, body fluid, or breath early in the progression of the disease. Amino acids, carbohydrates, and other metabolites should be detected and differentiated with great speed and micromolar sensitivity using these in-vitro devices [30]. Amino acids serve as the fundamental structural and functional components for human protein molecules. Several research works have observed the change in amino acid spectra in THz and sub-THz frequency ranges. For example, 20 amino acids were examined in the range of 0.2–2.8 THz and 0–6 THz by Wang et al. [31] and Yi et al. [32]. The change in spectral characteristics of different amino acids has been used to develop high-resolution THz microfluidic devices capable of measuring bovine

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serum albumin’s absorption spectra from a 10 pmol sample with a frequency range of 0.5–2.5 THz [33]. A sensitive and selective distinction between carbohydrate molecules and micromolar sensitivity was established using a THz nanoantenna sensor chip (0.5–2.5 THz) [34]. Likewise, a blood glucose measuring system was created in the sub-THz band, which provides the highest level of sensitivity in non-invasive THz-based blood glucose sensors with reported sensitivity of 15 mg/dl [35]. This application will be discussed in greater detail in Chap. 4. Proteins: Biological processes requiring variable protein activity rely on molecular recognition owing to the specificity of proteins [36]. The THz spectrum is characterized by intermolecular modes, internal motion, or lattice vibrations in crystalline materials. Since collective modes of vibration have a significant effect on the molecular conformation and structure in the THz range, the position and strength of molecules in this range are susceptible to protein structure. Hence a variety of THz-related research is ongoing in this important area. For example, a membranebased imaging approach for label-free protein identification using a susceptible technique has been developed in [37], which can be used for drug discovery and antigen–antibody reaction tests.

1.2.1.2

Pathological Examination of Body Fluids

THz radiation’s sensitivity to water and biomolecules allows its use to determine body fluid compositions, including blood, lymph, and urine. Characterization of body fluids assists in the diagnosis of various diseases, such as diabetes and blood cancer. Ahmed et al. presented a method by testing the samples in the sub-THz to THz frequency range (0.2–1.4 THz) for discriminating diabetes and non-diabetic human blood plasma (Fig. 1.4) [38]. For studying the sensitivity of THz waves for different blood components like Red Blood Cells (RBCs), plasma, haemoglobin,

Fig. 1.4 The refractive index and sensitivity with respect to frequency for different blood components in 1.5–3.5 THz band [38]. Reprinted with permission from IEEE

1.2 Terahertz Biomedical Applications

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White Blood Cells (WBCs), and water, a new partial type-b crystalline core with more compact cladding in hexagonal packing for use in a photonic crystal fiber (CCPCF)-based optical sensor has been proposed in [38], offering a superior relative sensitivity response of around 80% at f = 1.5 THz (see Fig. 1.4). In addition to this, several other works have developed sub-THz and THz biosensors for the detection of body fluids, including urine, blood plasma, and bovine serum albumin (BSA) for the detection of various diseases like prostate and thyroid cancer [39–41].

1.2.1.3

Gas Spectroscopy

Human breath contains thousands of volatile organic compounds (VOCs), and hundreds are linked to disease. Asthma, lung cancer, and colon cancer may be detected through exhalation and flatus gases. THz spectroscopy can be used as a high-performance gas sensor because the rotational energy of various gas molecules corresponds to specific THz frequencies [30]. In the sub-THz range (200–300 GHz), breath gases such as acetone, methanol, and ethanol exhibit more than 1000 absorption lines [42]. Additionally, the absorption lines are as narrow as a few MHz at pressures as low as few Pa, resulting in little to no spectral overlap. Molecular fingerprints can be obtained from spectra and absolute specificity and selectivity, which are obtained using a sub-THz spectrometer [43]. Various technologies have been developed using these spectral characteristics of various gases, including systems based on SiGe BiCMOS circuits [44], 65 nm bulk CMOS sensors [45], and time-domain THz spectrometers [46–48].

1.2.2 Terahertz in Imaging The THz radiation has been demonstrated for biological and medical applications as early as the 1990s when Hu and Nuss demonstrated THz imaging on a dried leaf [49]. Since that time, researchers have explored THz applications in a variety of fields. Rayleigh scattering plays a crucial role in deciding the penetration depth of the EM wave [50]. Rayleigh scattering can be overcome by increasing the wavelength of light to overcome the problems at greater depths, as Rayleigh scattering is inversely proportional to the fourth power of wavelength λ [51]. Nevertheless, the relatively long wavelengths present a problem of acquiring spatial resolution [52], which means that the wavelength must be small enough to enable good resolution yet large enough for severe losses due to Rayleigh scattering. The significant advantage of THz radiation in imaging applications is that it produces less scattering than the infrared and visible frequency and better spatial resolution compared to the microwave imaging modalities. Furthermore, in contrast to X-rays, which can damage molecules, THz radiation presents a significant medical imaging advantage over X-rays due to its low energy in the milli-electron-volt range.

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Although, THz waves cannot penetrate the human body deep because tissues contain various components, including a large quantity of water [53]. As a result, exvivo tissue imaging is the primary method used in most studies, and in-vivo imaging is rare. Few of the works done in the THz biomedical imaging domain are discussed below.

1.2.2.1

Excised Tissue Imaging (Ex-Vivo)

In 1976, the absorption of biological materials and refractive index characteristics were first determined in [54]. Since then, several research groups have investigated the excised tissue samples to observe the change in THz spectra for tumorous and healthy tissue. For instance, freshly excised brain tissue was analyzed using a reflection-based THz imaging setup in [54]. Similarly, [55–57] analyzed the excised breast tissues to study the possibility of using THz waves for healthy-tumor tissues differentiation. THz pulsed imaging was conducted on freshly excised human colonic tissues, producing 82% sensitivity and a 77% specificity for differentiating between healthy and tumor tissue, 89% sensitivity for the differentiation between dysplastic and healthy tissues, and 71% specificity for the distinction [58]. The most common biomedical application of the THz tissue distinguishing properties based on water content is Tissue-Conserving Surgery (TCS). To ensure that all tumor cells are removed, the surgeon should have the ability to diagnose and visualize tumors during surgery in order to prepare for excising and evaluating layers of tissue. This will allow the surgeon to preserve as much healthy tissue as possible during surgery. As a result of the increasing use of TCS, cancers such as skin and breast are being treated with greater success [59]. Recently, there has been growing interest in exploring the THz spectrum for TCS applications, especially in breast-conserving surgery [60, 61], in which THz pulsed imaging setups are used for the classification of tumor margins, as shown in Fig. 1.5. The details of other technologies and comparison with the THz technology currently under research for tumor margin assessment in TCS will be discussed in Chap. 5.

1.2.2.2

Cancer Imaging

THz imaging is mainly used to diagnose superficial layer cancers, such as skin cancer and oral cancer since the THz waves cannot penetrate deep inside the human body. Few of the THz technologies developed for skin and oral cancer detection are discussed in the following sub-sections: Skin Cancer: Researchers from TeraView Ltd. have found that using THz imaging, it is possible to detect skin cancer hidden beneath the skin in-vivo using a case study of patients with basal cell carcinoma in 2004 [62]. Several works have demonstrated that the THz imaging procedure can be used in identifying not only skin cancers

1.2 Terahertz Biomedical Applications

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Fig. 1.5 a Illustration of THz pulsed imaging (TPI) in reflection mode used for breast tumor margin assessment. b Imaging of different types of freshly excised breast tissues using TPI [56]. Reprinted with permission from IEEE

but also abnormalities in the skin that, if left untreated, could lead to skin cancer [21, 63–65]. Although THz light is extremely sensitive to water, meaning that the signal is heavily attenuated and penetrates very little into tissue, it would be difficult to perform THz scanning on deep tumors. Hence, this application depends significantly on the progress made in improving signal processing and improving the Signal-to-Noise Ratio (SNR) of the THz instrumentation [66]. This application will be discussed through various examples in Chap. 6. Oral Cancer: Most dental hospitals worldwide use X-ray-based techniques to diagnose dental caries and other dental problems like oral cancer. Terahertz Pulsed Imaging (TPI) is a new technology that can be utilized for dental caries detection replacing ionizing X-ray imaging. A study recently examined the differences between healthy and carious permanent and primary teeth by utilizing THz time-domain spectroscopy (THz-TDS) [67], suggesting the possibility of using THz imaging for assessing dental structures. Furthermore, a feasibility study was conducted by analyzing seven oral tissues resected from four patients based on THz imaging at frozen temperature to detect oral cancer [68]. Similarly, Yadav et al., in their recent work on the diagnosis of dental problems, demonstrated the use of the THz parametric imaging system to investigate different tooth samples in two and three dimensions [69] (Fig. 1.6). THz imaging has become practical for applications in the detection of cancer invivo, which requires a lot of work on the side of device development and technologies to use for these applications.

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Fig. 1.6 a Experimental setup of the TPI system, b tooth imaging using TPI system. Reprinted with permission [69] © 2021 University of Electronic Science and Technology of China. Publishing Services provided by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.

1.2.2.3

Endoscopy and Otoscopy

The endoscopic imaging system offers high flexibility when it comes to studying the interiors of tissues and organs. Researchers have worked on the development of THz endoscopes, such as the bronchoscope (bronchi), otoscope (ear), and laparoscope (abdomen). Doradla et al. provided the design of a single channel continuous-wave THz imaging system and tested the feasibility of colonic tissue endoscopy [70]. Similarly, an optical fiber coupled THz endoscope system was developed with having cross-section of (2 mm × 4 mm) × 6 mm (generator and detector head dimensions) to measure the sidewall of the tongue, palm skin, and mouth [71]. The same group also developed a THz otoscope that detects otitis media (inflammation or infection located in the middle ear), as shown in Fig. 1.7.

1.2 Terahertz Biomedical Applications

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Fig. 1.7 a Experimental setup of the THz endoscope, b measured THz spectra for different tissues using THz endoscope. Reprinted with permission from [71] © 2009 Optica Publishing Group

1.2.3 Terahertz in Treatment Prior to genetic mutation, DNA methylation plays a vital role in carcinogenesis [72]. A novel method for detection and control of cancer can be achieved by assessing and manipulating DNA methylation using THz radiation due to the abnormal methylation found in most cancer cells. THz spectroscopy at approximately 1.65 THz can be used to directly observe this epigenetic chemical change caused by high-power THz radiation [73].

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1 Terahertz Spectrum in Biomedical Engineering

Demethylation of DNA

THz dissociates methyl-DNA bonds, resulting in a decreased degree of methylation, also known as demethylation [74]. In THz demethylation, methyl groups are removed from DNA molecules using resonance energy, one of the biological modifications associated with high-power THz radiation. THz radiation can be used to alter DNA methylation in an epigenetically controlled manner using this method. Recent research by Son et al. has demonstrated that using a specific resonance frequency of 1.65 THz for cancer therapy using DNA demethylation can be performed without invasive, non-ionizing, and non-labeling THz techniques [75]. Similarly, the study explores the possibility of cancer treatment using a regenerative amplifier (SpectraPhysics, Spitfire) delivering 0.8 mJ of energy per pulse at a repetition rate of 1 kHz and a wavelength of 800 nm [76].

1.3 Terahertz Instrumentation: From Photonics to Electronics Physicists and engineers have actively worked to fill the “Terahertz gap” in the sub-THz and THz bands (0.1–10 THz), which was primarily caused by the lack of efficient sources and detectors for this range [77–81]. At lower frequencies, the movement of electrons in electronic devices serves to produce and detect microwaves and millimeter waves. Classical physics adequately describes the electromagnetic radiation in this spectrum region by treating it as a wave. NIR and visible light are produced by an optical device, in which electrons jump over the semiconductor bandgap when the electrons are excited. A better understanding of radiation may be gained by seeing it as a particle. The sub-THz and THz frequency ranges are between the electronic and optical portions of the electromagnetic spectrum, making it challenging to utilize electrical or optical equipment [82]. In electronic devices, an increase in frequency leads to an increase in highfrequency alternating currents, which causes a decrease in device power due to the introduction of unwanted resistance and capacitances and a reduction in electron mobility. These have been overcome by the miniaturization of circuits and introduction of high oscillating electrons [83]. Similarly, in photonic devices, the electronic bandgap is not sufficiently present in the materials to emit THz waves. The energy gap is too narrow to manage the modest discrete energy jumps required to emit photons with THz frequency. Cooler temperatures assist this process, but they also make equipment more costly, heavier, and less practical to operate [84]. Efforts have been made to develop high-powered and efficient electronic and photonic generation and detection devices with their own merits and demerits in the sub-THz and THz bands. Laser-based technologies utilizing femtosecond lasers and photoconductive antennas are being investigated for emission and detection on the photonics side, while integrated, compact chip-scale III–V semiconductor

1.3 Terahertz Instrumentation: From Photonics to Electronics

13

technologies utilizing silicon-based devices (CMOS, SiGe) and III–V devices (InP, HEMTs, and HBTs) are being investigated on the electronic side. The following sections give an overview of these technologies and their merits and demerits in the sub-THz and THz spectrum.

1.3.1 Terahertz Photonics Devices and Techniques This section provides a brief overview of THz generators and detectors available in the photonics domain and their use in current biomedical applications. There are two types of THz technologies based on laser types used to generate the THz signals: pulsed (time-domain) and continuous-wave (CW) (frequency-domain) technologies. THz radiation with a pulsed waveform offers a broad bandwidth and allows measurements to be made that are quick. However, it has a very low-frequency resolution, typically between multiple GHz. CW THz is created by combining two different frequency laser beams, which offers more control over the frequency and high-resolution imaging [85].

1.3.1.1

Pulsed Terahertz Technologies

Femtosecond lasers are used to produce ultrashort optical pulses. As a gain medium, femtosecond laser systems commonly use titanium-doped aluminum oxide (Ti: Sapphire), in which Ti3+ ions (0.1%) replace Al3+ ions in a sapphire matrix. In addition to its incredibly broad gain spectrum ranging from 650 to 1100 nm, Ti: Sapphire can handle high optical pumping powers (∼20 W) due to its high thermal conductivity, and the carrier lifetime (∼3.2 ms) is relatively short [86]. The typical experimental setup for the generation and detection of THz pulses in TDS using a femtosecond laser is shown in Fig. 1.8a, which is discussed in detail in [87]. The optical beam is divided into two portions using a beam splitter, as shown in Fig. 1.8a, one of which is translated to create a temporal delay. The emitter receives the optical pump pulse, producing a THz pulse and launching it into free space. That THz pulse then focuses on the detector. Probe pulses measure the THz-induced transients in the detector. An optical delay line, used to alter the optical path length, delays the laser pulses at the receiver from the emitter. An impinging beam is reflected by a retroreflector constructed of a prism (corner cube) or, better yet, perpendicular parabolic mirrors. A biased photoconductive (PC) antenna or Optical Rectification (OR) methods are used to generate and detect sub-picosecond THz pulses, as demonstrated in Fig. 1.8b, c. The properties of the laser pulse determine THz spectrum bandwidth. An example of a 100 fs pulse would correspond to a spectral width of 4–5 THz. Emitters and Detectors Based on Photoconductive Antennas: During light exposure, semiconductors and insulators become increasingly conductive, making them

14

1 Terahertz Spectrum in Biomedical Engineering Delay Line Δt

Beam Splitter

Photo Conductive Antenna

Femtosecond Laser Detected Pulse Transmitted Pulse

Femtosecond Laser Pulse

THz Pulse

Detector

Emitter

(b) THz Parabolic Mirrors

(a)

Femtosecond Laser Pulse

THz Pulse

Non-Linear Medium

(c) Fig. 1.8 a Experimental setup of generation and detection of THz pulses using femtosecond laser in THz-time domain spectroscopy with b photo conductive antennas as emitter and detector or c non-linear crystal as emitter and detector (Optical rectification)

suitable for use as photoconductive antennas. It consists of semiconductor material with two metallic contacts separated by a photo conducting gap, as shown in Fig. 1.8b. When a femtosecond laser pulse illuminates the photo conducting gap, electrons and holes are generated in greater numbers, resulting in the resistance drop of the device. Photon energy must be sufficient to overcome the material’s bandgap. The photoconductive antenna’s switching action must be in sub-picosecond to transmit or detect THz radiation, relying on the rapid acceleration of free carriers in opposite directions by the biased field applied at the electrical contacts. Hence, a short carrier lifetime is as important as a short laser pulse duration for ultrafast photoconductive switching. Optimum photoconductive materials have high carrier mobility and breakdown voltage. LT-GaAs, indium phosphide (InP), chromium-doped gallium arsenide (CrGaAs), radiation-damaged silicon-on-sapphire (RD-SOS), and amorphous silicon are some of the candidate materials for PC switches [87]. Emitters and Detectors Based on Optical Rectification: OR is a non-linear optical process that produces a quasi-DC polarization in a non-linear medium when an intense optical beam passes across it. The non-linear optical effects media, such as GaAs [88], diethyl amino sulfur trifluoride (DAST) [89], zinc telluride (ZnTe) [90], and lithium niobate (LiNbO3) [91] can also be used to produce THz pulses. Based on their efficiency and performance, J.L Coutaz, F. Garet, and V.P. Wallace have compared various THz TDS generation and detection techniques [92].

1.3 Terahertz Instrumentation: From Photonics to Electronics

1.3.1.2

15

Continuous-Wave Terahertz Technologies

Different technologies are available to generate and detect CW THz radiation in the electronics and photonics domain. In the electronics domain, Gunn diodes, tunneling diodes, backward-wave oscillators (BWO), and Schottky diodes-based frequency multipliers are the commonly used techniques. In the photonics domain, photo-mixing of lasers is the most common technique for generating CW THz pulses, which involves mixing two lasers with very similar frequencies [87]. The most common lasers used in CW generators are diode lasers, offering spectral ranges between 800 and 850 nm. A photo mixer is a solid-state device, including a photoconductive antenna, to illuminate the combined laser pulses like those used in pulsed THz technologies. Figure 1.9 shows the schematic setup of CW THz generation and detection setup using dual laser technology. There are many benefits and drawbacks to frequency-domain (CW systems) and time-domain (TDS) methods. TDS systems can provide more data in terms of depth information, the optical length of the substrate, and the existence of scattering interfaces in the sample [85]. Although, in imaging and other applications where this extra information is not required, TDS systems’ advantages no longer exist. On the other hand, CW systems are more compact and portable than time-domain setups [93]. In addition, due to the high cost of femtosecond lasers, pulsed THz photoconductive antenna setups are more expensive than CW photoconductive antenna setups combined with a lower spectral resolution [94]. The CW systems can provide similar information as TDS setups but require extra hardware and additional scans to recover all the data like depth analysis, optical length, and scattered intensity within

Fig. 1.9 Schematic of generation and detection of THz pulses using photonics based continuouswave laser setup

16

1 Terahertz Spectrum in Biomedical Engineering

the sample. The efficiency of both pulsed and continuous waves is undesirable and requires improvement [95]. Various techniques are being studied to improve the efficiency of the CW THz systems, including enhancements in excitation methods, semiconductor parameters, and radiation efficiencies of photoconductive antennas [93, 95–98]. The other laser techniques currently under research for CW THz generation and detection are free-electron lasers [99, 100], gas lasers [101], and Quantum cascade lasers (QCL) [102–104]. The free-electron lasers can produce high powers and are tunable in a wide frequency range but are bulky and expensive [105]. The gas lasers utilize infrared lasers to pump polar molecules and possess one of the highest THz output powers, up to 100 mW. In contrast, they use higher power and are inefficient [86]. In THz QCLs, inter-sub-band transitions in GaAs/AlGaAs quantum wells interact to produce desired wavelengths of emitted radiation. Despite their compact size, these lasers are difficult to tune across a wide frequency range. In addition, most cryogenic cooling systems are used when they work at low temperatures [87]. THz devices based on photonics have already been applied in scientific laboratories and industrial research centers during the last two decades due to research and development in the THz domain. Figure 1.10 shows photonics-based THz spectroscopy setup used for various biomedical applications. Figure 1.10 represents a THz-TDS system utilizing GaAs photoconductive antennas as emitter and detector for differentiating soft protein microstructures [106]. Similarly, Sun et al. used a THz time-domain reflection setup manufactured by Menlo Systems GmbH, Planegg, Germany, for in-vivo water diffusivity measurements in occluded human skin [107]. To make effective use of CW and pulsed THz systems, they must be evaluated according to their unique advantages and disadvantages. As far as THz photo-mixing is concerned, non-linear photoconductors with a very short carrier lifetime, such as LT-GaAs, are still the most common materials. The properties of these materials

Fig. 1.10 THz-TDS setup utilizing GaAs photoconductive antennas as emitter and detector for differentiating soft protein microstructures (Reproduced with permission from [106] © 2009 Optical Society of America)

1.3 Terahertz Instrumentation: From Photonics to Electronics

17

are difficult to control, and fabrication processes are very costly [85]. In addition, the overall THz photonics setup based on pulsed and CW technologies is bulky and complicated. A delay line in the pulsed THz time-domain spectroscopy must not move the laser beam by more than a micrometer (1/1000th of a millimeter). For instance, the delay line must be focused on a gap between photoconductive antennas, which is a few micrometers wide. Additionally, recordings tend to be longer, and one must check throughout this lengthy recording period that the laser intensity does not fluctuate [92].

1.3.2 Terahertz Electronic Devices and Techniques There is a need for compact, portable, and easy to integrate technologies for various biomedical applications, including cancer imaging and non-invasive diagnostics, which can be addressed by the electronic THz technologies [108]. When it comes to electronics, the most widely used technologies for CW THz generation and detection include solid-state frequency multipliers driven by microwave sources [109, 110], heterojunction bipolar transistors (HBTs) [111, 112], high electron mobility transistors (HEMTs) [113, 114], Gunn-diode based fundamental harmonic oscillators [115], and GaAs based Schottky diodes [116]. The basic principles of their working can be found in the literature [86, 117, 118]. In this book, we will primarily concentrate on the requirements of THz generators and detectors for biological applications.

1.3.2.1

From Terahertz to Sub-Terahertz Electronics: Sources and Detectors

THz power is a significant issue for biomedical imaging and sensing applications since it is required for many applications. The available output power by various state-of-the-art electronic and photonic sources is shown in Fig. 1.11. In the planar THz sources, the HBT Microwave Monolithic Integrated Circuits (MMIC) based on InP and SiGe semiconductor materials can generate power levels in 0.1–100 mW range in the frequency range of 0.1–1 THz [111, 112, 119–121]. Similarly, InP-based HEMT has similar power generation capabilities as HBT technology in 0.1–200 mW [122]. The maximum output power reported to date for CMOS-based THz sources is ~0.8 mW which uses a harmonic oscillator with frequency multiplication at 300 GHz and ~0.1 mW at 500 GHz using multiplier [123]. A few examples of the planar THz sources based on HBT and HEMT technologies with their respective output power are shown in Fig. 1.12. Microwave technology is another class of solid-state THz sources. Solid-state THz sources work by multiplying incoming microwave frequencies with a microwave synthesizer’s output frequency in the range of 10–100 GHz as a seed. Until now, Schottky diode multiplier chain-based solid-state sources have been able to deliver milliwatt output power up to 1 THz and microwatt output power up to 3 THz [109, 116, 124]. Figure 1.11 also denotes the output power available from

18

1 Terahertz Spectrum in Biomedical Engineering

Fig. 1.11 Output power versus frequency plot for various electronic and photonic THz sources: MMIC, Microwave monolithic integrated circuits; HBT, Heterojunction bipolar transistors; CMOS, Complementary metal oxide semiconductor; HEMT, High electron mobility transistors; DFG, Differential frequency generators; QCl, Quantum cascade lasers

the photonics THz sources, including photo mixers, QCLs, optical rectification, and Difference Frequency Generators (DFGs) obtained from the state-of-the-art literature [90, 104, 125–129]. The typical power of radiofrequency sources tends to fall as the inverse of the frequency squared, as evident in Fig. 1.11. Hence, electronic sources are suitable for applications in the sub-THz and THz ranges from 0.1 to 2 THz. After 2 THz, the output power level of these devices falls to few μW. In contrast, photonic sources like lasers and photo mixers tend to lose power with decreasing frequency (see Fig. 1.11). Furthermore, they are ideal for applications requiring higher THz (>1 THz). The THz detectors also play an important role in diagnostic and imaging applications by sensing the wave projected from the source onto the sample under test. These detectors are evaluated in terms of their sensitivity, which is often evaluated in responsivity and Noise Equivalent Power (NEP). Voltage responsivity is defined as the detected voltage level divided by the applied electromagnetic radiation’s input power if the detector completely absorbs the rays. When a device’s responsiveness is higher, it indicates that it is performing better. The NEP is defined as the temperature change for incident radiation that results in an output signal equal to the root mean square of the noise level [132]. A smaller NEP correlates to a more sensitive detector in terms of sensitivity. The electrical NEP is defined as the signal power absorbed in the detector. Instead, the optical NEP is often a signal power incident on

1.3 Terahertz Instrumentation: From Photonics to Electronics

19

Fig. 1.12 Planar electronic THz sources: a three-stage, 16-PA cell combined 220 GHz PA based on InP HBT MMIC delivering output power of 200 mW at 210 GHz [130]. b A five-stage 300 GHz PA MMICs in InGaAs mHEMT technology delivering output power of 23 mW at 300 GHz [131]. Reprinted with permission from IEEE

the detector system. The optical NEP is equal to the electrical NEP divided by the optical coupling efficiency of the detector system [132]. In general, there are two types of THz detection schemes: coherent and incoherent. A coherent detection method measures phase and amplitude, whereas an incoherent detection system only measures intensity. Insofar as they share underlying mechanisms and key components, coherent detection techniques are closely related to generation techniques [118]. Photonics pulsed and CW THz detection techniques using photoconductive antennas and optical rectification are coherent. These have been discussed in the previous section of Photonics THz devices and techniques. Similarly, in electronics, heterodyne detectors using mixers provide both amplitude and phase information being coherent. A heterodyning signal mixes two different frequency signals into a separate sum and difference signal, thereby shifting the detection frequency range. For THz heterodyne detection, we usually focus on the difference frequency component (down-conversion). The simultaneous use of heterodyne THz detection and THz imaging can provide unprecedented sensitivity and spectral resolution among different detection schemes. The mixing process makes it possible to detect very weak terahertz signals by enhancing the detected THz radiation with the reference local oscillator. In addition, the noise power can be effectively reduced by limiting the detection bandwidth to the bandwidth of the Intermediate Frequency (IF) electronics [109]. VNAs work on the heterodyne mixing principle till

20

1 Terahertz Spectrum in Biomedical Engineering

1.5 THz and are available from a few companies. Examples include Keysight Technologies (formerly Agilent), VDI, Anritsu, Rohde and Schwarz, and AB Millimeter. In Chap. 2, we will discuss heterodyne mixing in more detail. Non-coherent devices include thermal and electronic detectors. Thermal detectors are the earliest used for THz radiation, including bolometers, Golay cells, and pyroelectric devices [133–135]. These detectors absorb the radiant energy released by the THz photon. When energy is deposited into the detector, it raises its temperature, and this temperature rise is recorded. Several thermometric properties play a role in this conversion. Thermal expansion, for instance, occurs when heat is applied to a Golay cell. By analyzing the expansion of a bag of gas, heat input is calculated so that the THz radiation energy may be evaluated. A change in temperature causes an increase or decrease in electrical resistance in the semiconducting bolometer. Pyroelectric materials produce a change in electric potential when heated. The detection of THz radiation can be achieved through any of these processes. Special radiation absorber elements are usually incorporated into the device to increase its efficiency by increasing the amount of absorbed radiation. In all thermomechanical systems, the responses are slow, and they are challenging to integrate. Electronic detectors can also process incoherent signals by utilizing rectification processes of electromagnetic signals, including Schottky diodes, CMOS ICs, and HEMT technologies [123, 136– 138]. The parasitic elements and their interface to the reader are the only limiting factors to the performance of these rectification type detectors. A few examples of incoherent detection using silicon technology are shown in Fig. 1.13. The performance of some coherent and incoherent sub-THz and THz detectors is summarized in Table 1.1. In general, coherent detectors have a better NEP than incoherent detectors (see Table 1.1). Those receivers that are based on electronics can work well in the sub-THz range, providing NEP values are in pW/Hz1/2 . In contrast, fW/Hz 1/2 is the NEP of photonics detectors in a higher-frequency region (>1 THz).

1.4 Artificial Intelligence in Sub-Terahertz Bioelectronics A growing number of possibilities in biomedicine and healthcare technologies are emerging that may also extend to THz and sub-THz technologies as artificial intelligence (AI), machine learning, and robotics continue to advance. Medical AI is primarily focused on developing algorithms that diagnose patients and recommend treatment. An AI system analyzes the level of acceptance of critical care decision support methods among healthcare providers. Data can be transferred and stored more efficiently through the integration of AI in the healthcare system. THz healthcare can automate its processes through AI, which combines multiple technologies to recognize, act upon, and respond, so they can instantly perform clinical and administrative functions. It is essential to improve procedures in THz and sub-THz domains to solve specific problems and enhance capabilities. AI is used in THz/sub-THz bioelectronics to provide advanced patient monitoring, diagnostic assistance, and medical

1.4 Artificial Intelligence in Sub-Terahertz Bioelectronics

21

Fig. 1.13 Incoherent terahertz detectors using silicon technology: a A 3.0 THz camera microphotograph and block diagram imaging a toothpick using 65 nm standard CMOS process with a roomtemperature responsivity of 526 V/W and a noise equivalent power (NEP) of 73 pW/Hz 1/2 [139], b A 128-Pixel 0.56 THz sensing array camera for real-time near-field imaging in 0.13 μm SiGe BiCMOS [140]. Reprinted with permission from IEEE Table 1.1 Parameters of some coherent and non-coherent THz detectors Detector type

Incoherent

Coherent

Operation frequency (THz)

Noise equivalent References power (NEP), (W/Hz1/2 )

Golay cell

≤30

1.4 × 10−10

Tydex Ltd. (Commercial)

Pyroelectric

2.52

1.2 × 10−10

[141]

Bolometers

1.8–3.1

10−10

[142]

Schottky diodes

0.1–1.5

3.7 × 10−10 10−9

[143]

Schottky diode (zero biased)

0.1–0.7

3.1 ×

65 nm CMOS FET

0.1–1.2

9.6 × 10−12

[145]

130 nm SiGe HBT (heterodyne)

0.6

0.6 × 10−12

[146]

40 nm CMOS (heterodyne)

0.3

0.1 × 10−12

[147]

InGaAs Photodetectors

0.0375, 0.166

10−11

[148]

1.81 × 10−15

[149]

CW: photoconductive 0.1–1 antenna

[144]

22

1 Terahertz Spectrum in Biomedical Engineering

data analysis. The application of AI includes many different areas, including image recognition and interpretation, medical diagnosis, treatment schedules, etc. We will discuss how AI is applied to sub-THz and THz technologies to identify diagnostics and visualize contrasts and absorptions in Chaps. 5 and 7. Chapter 8 examines how the emerging device automation poses innovative convergence issues regarding robotics and medical device development, design, and programming for information exchange, good communication, and configuration of the various devices. The analysis of networks of molecules, as opposed to individual molecules, can help us better understand and compare them with existing databases. In Chaps. 4 and 7, the instance of blood glucose measurements in the sub-THz range illustrates how this will allow us to develop more effective treatments.

1.5 Scope of the Book The goal of this book is to demonstrate the use of sub-THz sensor technology for biomedical applications. Its objective is to evaluate how the sub-THz spectrum can be used for tissue characterization and to introduce various experimental cases, ranging from phantom fabrication to process evaluation. Throughout the book, we provide experimental examples drawn from our work as well as the literature on biomedical imaging and sensing applications, and we advise the reader on the best procedure to follow to calculate the desired parameters. We assembled this book to benefit students and researchers who wish to investigate the spectrum below the THz frequency and apply it for a range of applications in different sectors of the industry and laboratories. Additionally, using different algorithms and techniques, we introduce machine learning, in which imaging and sensing become more efficient than the stand-alone data acquiring and processing methods. The book is organized into eight chapters. Chapter 2 discusses compact sub-THz instrumentation using heterodyne receivers in the electronics arena and different probes for sensing applications. Chapter 3 presents methods for the dielectric characterization of biological samples in the sub-THz region and developing dielectric models for various biological materials including saccharide solutions, blood, protein, and various tissues. Chapters 4, 5, and 6 describe the use of these subTHz technologies by discussing three scenarios, namely, non-invasive blood glucose measurement, assessing breast tumor margins, and examining skin cancers. Chapter 7 addresses how machine learning is used in conjunction with sub-THz instruments to make the overall measurement setup more useful for practical applications by converting the sub-THz equipment information into more legible terms for readers in diagnosis applications and improving image processing techniques in imaging applications. Finally, Chap. 8 discusses some advanced topics in sub-THz imaging automation to make scientific imaging faster and easier [150].

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Chapter 2

Electronic Sub-Terahertz VNA Measurement Techniques

Abstract Most biomedical applications rely on dielectric constant characterization for sensing and imaging applications; however, two well-known techniques in the sub-THz and THz frequency ranges can be used for this purpose. Both TimeDomain Spectroscopy (TDS) and dielectric characterization using a Vector Network Analyzer (VNA) are widely used in photonics and electronic engineering. Electronics implementations perform better (in terms of output power) than photonics implementations in the sub-THz region. Additionally, electronics technologies provide a more compact, more integrated, and more cost-effective platform. The purpose of this chapter is to describe the fundamental operation and measurement principles of THz VNA setups. The THz VNAs differ from conventional RF/MMW VNAs by an additional hardware configuration for upconverting the frequency (frequency extenders). The later section describes calibration techniques, the first step toward dielectric characterization using a VNA to improve measurement accuracy. Finally, at the conclusion of the chapter, we will present different dielectric characterization methods using sub-THz probes, including horn antenna and waveguide integrated into the VNA system for permittivity estimation of solids and liquids.

2.1 Introduction The importance of dielectric characterization at sub-THz frequencies is increasing owing to numerous applications at this frequency range. The dielectric properties of biological materials are necessary to quantify the interaction of EM fields with those materials in the biomedical field. It is essential to pay attention to the complex permittivity. Both sensing and imaging biomedical applications require precise determination of this parameter [1]. Dielectrics can be characterized in the sub-THz and THz domain by employing TDS and by vector network analyzer (THz VNA) [2]. Measurement with TDS topologies requires large bandwidth, but the optical components occupy much space, combined with a femtosecond laser, all of which need to be aligned as discussed in Chap. 1. Among TDS systems, scanning times are relatively

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. K. Koul and P. Kaurav, Sub-Terahertz Sensing Technology for Biomedical Applications, Biological and Medical Physics, Biomedical Engineering, https://doi.org/10.1007/978-981-19-3140-6_2

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long during repositioning an optical delay line due to the bandwidth and resolution they provide [3, 4]. Furthermore, electronics technologies have a higher output power at the generating end than conventional photonics technologies [5]. To understand the dielectric measurement methodology in a VNA setup, one should be familiar with THz VNA basics, which differ from conventional microwave VNAs in terms of hardware configuration. In the following sections, first, we will investigate the scattering (S) parameters measurements offered by the THz VNA, followed by the sources and detectors present inside the THz VNA architecture for emitting and collecting the response.

2.2 Terahertz VNA In 1990, Philippe Goy and his colleagues developed the first THz VNA, later patented and commercialized by AB millimeter Ltd. [6, 7]. A Schottky diode-based THz generator and detector that uses a Gunn oscillator for frequency multiplication is described in the study by Goy et al. [7]. When it came to the 8–180 GHz band, the first sub-THz VNA could operate without the need for frequency extension. However, one and two Gunn oscillators were required for the 180–500 and 500–800 GHz bands, respectively. THz VNA uses heterodyne mixing of the signal at the detector’s end for coherent detection, making the detection sensitive, linear, and vector. In addition to the AB millimeter, several companies supply commercial systems up to 1.5 THz these days, including Virginia Diodes Inc. (VDI), Rohde and Schwarz, Keysight Technologies, Inc., and Anritsu. Figure 2.1 illustrates an example of a THz VNA from Keysight Technologies (N5247B), developed with frequency extenders from Virginia Diodes, offering measurements from 0.1 to 1.1 THz and installed at the

Fig. 2.1 THz VNA from Keysight Technologies (N5247B), developed with frequency extenders from Virginia Diodes, offering measurements from 0.1 to 1.1 THz and installed at the Center of Applied Research in Electronics (CARE), Indian Institute of Technology, Delhi (IIT, Delhi)

2.2 Terahertz VNA

33

Center of Applied Research in Electronics (CARE), Indian Institute of Technology, Delhi (IIT, Delhi), India. Currently, five frequency extenders are available in the laboratory working in the frequency ranges of 110–170, 140–220, 220–330, 500–750, and 750–1100 GHz. A commonly used RF VNA measures the frequency response of the component that is placed between the source and the detector unit of the network. It detects the power of a high-speed signal entering and exiting the component. This is because power, in contrast to voltage and current, can be measured correctly at high frequencies, making it an excellent choice for signal measurement. At each frequency point, the high-frequency signal’s amplitude and phase are recorded in the same manner by applying CW at that frequency [8].

2.2.1 Scattering Parameters A VNA measures scattering parameters (S-parameters) with exceptional accuracy and these parameters describe the wave propagation inside the device under test (DUT). We should therefore identify the origin, meaning, and interpretation of Sparameters. VNAs are most commonly found with two ports, but there are VNAs with 3, 4, 8, and 24 ports commercially available in the microwave band, while there are VNAs up to four ports in the sub-THz and THz regions. Ports can take several physical forms, but most commonly, they are associated with some sort of guidedwave medium. Generally, coaxial connectors and cables are used at low frequencies (typically, below 100 GHz), but rectangular metallic waveguides are used at higher frequencies, as shown in Fig. 2.1. Several reviews have been published in recent years that have looked at rectangular waveguide sizes for sub-THz and THz frequencies and the frequency bands and interconnecting flange designs associated with them [9–11]. Above 100 GHz, there are established standards for waveguide sizes and interconnecting flange details [12]. An N-port network can be described by the ABCD parameters (the transmission matrix), the Z-parameters (the impedance matrix), the Y-parameters (the admittance matrix), and the H-parameters (the hybrid matrix) [13]. Voltages and currents at the ports of the network are all measured by these parameter sets. Microwave (MW) to THz networks use waveguides instead of simple two-terminal wires. In technical terms, transmission lines are waveguides in various forms such as coaxial cables, metallic waveguides, printed traces in an IC, or optical fibers. All these networking parameters are insufficient due to the high-frequency structure enclosed in the module, which lies beyond the electrical length between the cross-section of the port (the device connector) and the actual high-frequency system. If this length was adjusted, as is often required in the system’s design, the voltages and currents would change in a rather complex manner because they represent the reflections and incidences of voltages and currents along the interconnects [14]. Microwave to THz measurements is based on two waves in the frequency domain: port incident waves and reflected waves. Incident waves travel to the device, whereas

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Fig. 2.2 a One-port, and b two-port scattering parameters definitions and representations

reflected waves travel away. Symbolically, incident waves on the port are denoted by a, while reflected waves are represented by b. Since THz VNAs are typically two-port instruments, Figure 2.2 shows the conventions for the microwave to THz networks with a one and two-port configuration. In the context of one-port and twoport components, they may serve as a tool for measuring reflection and transmission characteristics, for example, antennas, amplifiers, transmission lines, etc. Wave quantities an and bn , with n representing the ports number, are complex numbers. Wave-quantity magnitude can be mathematically defined as the square root of the power carried by a traveling wave described in great detail in [14]. If we denote the power carried by the incoming wave at the nth port as Pn+ and by the outgoing wave as Pn− , then we have the wave amplitudes in W 1/2 (W = Watts) as, |an | =



Pn+ W 1/2 , |bn | =



Pn− W 1/2

(2.1)

The phases of these wave quantities are represented in terms of electric fields at the port: ∠an = ∠E n+ , ∠bn = ∠E n−

(2.2)

S-matrix (or scattering matrix) relates Wave quantities an and bn for the n-port network in vectorial form as follows: Sa = b

(2.3)

where, a = [a1 a2 a3 .....an ]T and b = [b1 b2 b3 .....bn ]T and S-matrix is given by:

2.2 Terahertz VNA

35

⎡

S11 ⎢ . S=⎢ ⎣ . Sn1

.. .. .. ..

⎤ S1n . ⎥ ⎥ . ⎦ Snn

(2.4)

Reflection coefficients are the diagonal elements of the S-matrix. In terms of ai and bi at the ith port, reflection coefficient (Sii ) can be written as follows: bi , k = i Sii = ai ak =0

(2.5)

Similarly, transmission coefficients are the off-diagonal elements of the S-matrix. The transmission coefficients (S ji ) are expressed as a function of incoming waves at the ith port and outgoing waves at the jth port as: b j S ji = , k = i ai ak =0

(2.6)

In Eqs. (2.5) and (2.6), ak = 0, k = i indicates that S ii and S ji are measured only when the excitation is applied to the ith port. At the same time, incident waves are absent from all the other ports requiring the other ports to be loaded with an impedance that does not reflect any power waves left behind. More details of the S-parameters can be found in [15]. Because the vast majority of biomedical measurements in the sub-THz and THz domains involve two ports, S 11 and S 21 are the primary parameters used in data analysis for these measurements. Regarding biomedical sample analysis, the value of |S 21 |< 1, implies that none of the outgoing waves can carry more energy than the incident wave. Unity magnitude S-parameter indicates complete transmission/reflection, whereas zero magnitude S-parameter reveals complete energy absorption by the device. A variety of other parameters can be directly computed while calculating the Sparameters of transmitted and reflected waves from Device Under Test (DUT). For instance, the transmission phase as a function of frequency can be used to determine the propagation delay through the DUT [16]. The measurement of time-domain Sparameters can also be achieved by applying the appropriate transformation of the measured frequency-domain S-parameters in the time domain using Fourier Transform [17]. We will be using these parameters for permittivity estimation and image analysis of biological samples in the later chapters.

36

2 Electronic Sub-Terahertz VNA Measurement Techniques

2.3 Generic VNA Architecture In the previous section, we illustrated using a VNA how EM waves are applied to the ports, how the amplitude and phase of measured response are measured in terms of incident (a) waves and reflected (b) waves, and how the ratio b/a is used to calculate S-parameters. Figure 2.3 depicts a generic 2-port VNA architecture. The details of the working of VNA can be found in many works reported in the literature, including [15, 18, 19]. We will give a brief description of the VNA architecture depicted in Fig. 2.3. A variable frequency continuous-wave source creates the test frequency, and a variable attenuator controls the power level of the source. In the DUT, the location of the switch determines which direction the test signal will go through it. For example, in the case where the switch is at position 1 as shown in Fig. 2.3, the test signal is incident on the DUT at port 1, which is adequate for measuring S 11 , and S 21 , respectively. The test signal is fed to the common port of splitter 1 by the switch. One arm of splitter 1 (the reference channel) feeds a reference receiver for port 1 (RX REF1), while the other arm (the test channel) connects to port 1 via the directional coupler

Fig. 2.3 Generic architecture of VNA

2.4 Terahertz VNA Architecture

37

DC1. When the power reflected from port 1 is coupled off to the third port of DC1, it is fed to the test receiver 1 (RX TEST1). Similarly, signals exiting port 2 flow through DC2 to reach RX TEST2 via DC2. As a result of sharing a common reference oscillator, RX REF1, RX TEST1, RX REF2, and RX TEST2 are referred to as coherent receivers because they are capable of measuring both the amplitude and phase of the test signal at the test frequency. When the switch is set to position 2, the test signals are applied to port 2, RX REF2 measures the reference, reflections from port 2 are coupled off by DC2 and measured by RX TEST2, and signals leaving port 1 are coupled off by DC1 and measured by RX TEST1. This position is used to determine S 22 and S 12 . There are two technical prerequisites for this generic VNA: One need is that the cables connecting the port and the measuring plane be suitably long. The other is that RF sources operating in the desired frequency range be available. These two requirements are met up to a frequency of 67 GHz, for which V-connectors and 1.85 mm cables can be used, and beyond that, they must be met by other means. Higherfrequency tests require wires no more than 20–25 cm in length, and generators with accurate power control are not widely available. As a result, the approach taken for sub-THz and THz VNAs is distinct [20].

2.4 Terahertz VNA Architecture A sub-THz to THz VNA working in the frequency range of 0.1–1.5 THz uses synthesized sources based on various frequency multiplication technologies, usually, harmonic multipliers based on Schottky diodes, like those described in [21]. A lowerfrequency synthesized source is then used to drive the harmonic mixer. In addition to the basic RF VNA described in the earlier section, frequency extenders/multipliers are employed to generate and detect sub-THz and THz signals. Figure 2.4 depicts the frequency extender configuration used for this purpose as described in detail in [22].

2.4.1 Sub-Terahertz/Terahertz Frequency Translation Using Extenders Figure 2.4 shows a frequency extender module comprising a transmitter powered by the RF signal from the RF VNA and two-directional couplers, which are positioned back-to-back. The reference channel receiver (or reference mixer) is used to sample the forward wave, and the measurement channel receiver (or measurement mixer) is used to sample the return wave. According to the heterodyne concept, both signals from the VNA (RF and LO) are configured with a slight relative frequency offset in order to switch the measurement signal to an intermediate signal. The magnitude

38

2 Electronic Sub-Terahertz VNA Measurement Techniques

Fig. 2.4 The following is an example of a VDI frequency extender, along with the configuration. * symbol represents attenuator used for 140–220 GHz band and above and isolator for lower sub-THz bands

and phase information contained in the measurement signal must stay intact during the measurement process. The RF input signal is amplified within the transmitter, and a multiplier is employed to transform the input signal to a higher-frequency range. The power of the signal is routed through a pair of back-to-back directional couplers at the transmitter output. A sample of the incident and emerging signals are extracted using directional couplers in the scattering parameters measurement. One coupler samples the power that is being transmitted out, while the other coupler samples the power that is being returned from the test port. S 11 indicates that the signal is transmitted by this module and reflected off from the DUT, S 12 indicates that the signal is transmitted by a separate module and passed through the DUT. These reference and test signals must be converted (heterodyning) to IF, where the magnitude and phase can be measured directly (digitalized). Both reference and test signals are translated at the same frequency, preserving the phase relationship between them. For this purpose, THz VNAs typically use sub-harmonic mixers, which enable a lower local oscillator (LO) frequency. An incoming sub-THz/THz signal is down-converted to an intermediate signal within the bandwidth of the instrument. These devices extend the frequency range of a receiver cost-effectively and are mostly used in the spectrum and signal analyzers. A detailed discussion on the subharmonic mixer can be found in [23, 24]. In [21, 25, 26], a more detailed explanation

2.4 Terahertz VNA Architecture

39

Fig. 2.5 Block diagram of sub-THz/THz measurement setup of THz VNA demonstrating the connection of RF/MW VNA with frequency extenders

of the advantages of heterodyne detection, including the possibility of achieving a high dynamic range through narrowband filtering, is presented. Figure 2.5 shows the overall THz VNA block diagram as described in [27], where the MW VNA unit working up to 67 GHz band upconverts the frequency in sub-THz and THz discrete bands using frequency extenders which has been earlier shown in Fig. 2.4. The subTHz/THz signal is projected onto the DUT using the Tx-Rx Frequency extender connected to port 1 of the MW VNA unit, which collects the S 11 parameter reflected from DUT. The Rx Measurement unit connected to port 2 of RF/MW VNA is used to measure the S 21 parameter.

2.4.2 Terahertz VNA Performance VNAs require a more Dynamic Range (DR); therefore, it is more important to understand the dynamic range and how it affects measurement accuracy. In VNA, the DR is determined by the difference between the maximum output power and the noise level [28]. The noise level is equivalent to the minimum input power the vector network analyzer can handle since lower signals would be lost in the noise. An ideal input signal should be between these two values (maximum output power and noise level) for optimal signal acquisition and measurements. Noise in the VNA is determined by the VNA’s IF bandwidth, signal output power, and averaging factor. Acceptable settings for noise characterization are those that are within certain tolerance of those in the measurement (Keysight recommends that the IF bandwidth should be within a factor of 10 and the source power within a factor of 10dBm) [29].

40

2 Electronic Sub-Terahertz VNA Measurement Techniques

Table 2.1 Dynamic Range and test port power of different sub-THz and THz bands frequency extenders provided by VDI technologies Waveguide band (GHz) Frequency band (GHz) Dynamic range (BW = 10 Hz,dB) WR6.5

Standard

Typical Minimum

110–170

120

110

Test port power (dBm)

13

WR5.1

140–220

120

110

6

WR4.3

170–260

115

110

4

WR3.4

220–330

115

105

1

WM710 (WR2.8)

260–400

100

80

−10

WM570 (WR2.2)

325–500

110

100

−3

WM380 (WR1.5)

500–750

100

80

−25

WM250 (WR1.0)

750–1100

65

45

−30

WM164 (WR0.65)

1100–1500

60

40

−45

The typical DR of VDI frequency extenders for 10 Hz IF bandwidth and test port power for different sub-THz and THz bands is summarized in Table 2.1. The DR and test port power decrease with an increase in frequency due to the reduction in output power levels by the Schottky-based sub-THz/THz source, as pointed out in Chap. 1. Similarly, the NEP values of the heterodyne receivers reduce with frequency from sub-THz to THz region, thus reducing the overall DR of the higher-frequency band extenders. Since the biomedical imaging and sensing applications require higher test port power for better penetration inside the tissues and biological samples, we will concentrate on the higher output power sub-THz VNAs for dielectric characterization and biological imaging applications in the following chapters.

2.5 Terahertz VNA Calibration Systematic, random, and drift errors are the three types of measurement errors that can occur in network analysis [30]. Correctable systematic errors are those that are repeatable and that the system is capable of measuring. These errors are caused by mismatch and leakage in the test setup, measurement signal path loss, internal reflections and system frequency response, and isolation between the reference and test signal paths. When there are non-repeatable random and drift errors, the system is unable to measure and correct them. These errors include variations in measurement due to noise, frequency and temperature drift, and other physical changes in the test setup that occur between calibration and measurement. The systematic errors that occur in high-frequency measurements are the most significant source of measurement uncertainty. The measurement obtained as a result is the vector sum of the response of the test device plus all error terms. Hence, it is possible to combine

2.5 Terahertz VNA Calibration

41

Fig. 2.6 Different sources of systematic errors in VNA

all these errors linearly to correct the measurement response using a calibration procedure. Figure 2.6 shows the major systematic errors associated with VNA [31]. Errors in directivity limit the DR of reflection measurements, while errors due to crosstalks limit the DR of transmission measurements. Mismatched sources and loads cause signal reflection errors. Errors resulting from source mismatches are caused by interactions between the source match of the test system and the input match of the DUT, while load mismatches are caused by interactions between the load match of the test system and the output match of the DUT. Lastly, there are error classes based on the frequency response of receivers, known as reflection and transmission tracking. “Tracking” refers to comparing S-parameter measurements between a receiver and a reference receiver. Consequently, the errors in frequency response are caused by a tracking issue between the test and reference receivers. Different error models have been derived for characterizing one- and two-port networks for standardizing calibration procedures which can be applied for highfrequency sub-THz and THz VNA measurements. The detailed procedures of error modeling of ports are given in [15, 32, 33] for quantifying inaccuracies in directivity,

42

2 Electronic Sub-Terahertz VNA Measurement Techniques

source and load match, isolation, and frequency response of the measurements [30]. Calibration ensures accuracy, regardless of whether the measurement plane is located at the connector on the instrument’s front panel or the connectors at the ends of test cable runs.

2.5.1 Calibration Techniques THz VNAs offer S 11 and S 21 measurements using rectangular waveguides at the test ports, which require uncertainty analysis in waveguide ports. Well-documented and more thorough uncertainty analysis for rectangular waveguides was developed by Bannister et al. in [34], along with a valuable set of tables for estimating the uncertainty of rectangular waveguide calibrations given their dimensional uncertainty. In addition, the European Association of National Metrology Institutes has published a document that describes VNA calibration uncertainty [35]. As mentioned earlier, the sub-THz/THz VNA setups include frequency extenders for different frequency bands, including the specific dimension of waveguide test ports; for example, the WR6.5 waveguide is used for the 110–170 GHz band. Similarly, WR 3.4 is suitable for the 220–330 GHz region and so on [22]. Each waveguide size and type of flange requires at least one calibration kit. A calibration kit needs to contain standards that can be defined on the VNA. This can often be done by using calibration kit definition data (usually available on disks or memory sticks) provided by the calibration kit manufacturer. Calibration involves measuring established standards and using the findings to determine the primary sources of measurement error. A complete 2-port calibration includes reflection, transmission, and isolation tests. The default calibration standards are a pair of short circuits, a thru, and a load. Figure 2.7 consists of a diagrammatic representation of flush-short, offset-short, fixed load, and line calibration standards for the waveguide test ports [36].

2.5.1.1

SSL/SOLT Calibration

The flush-short/offset-short/well-matched load technique is a common one-port calibration technique. This is referred to as the SSL calibration technique (short-shortload). SSL calibration is similar to the short-open-load (SOL) calibration method, which is often used in coaxial lines [37]. The SSL calibration is performed via two offset-shorts, i.e., replacing the flushshort with a second offset-short. When the offset-shorts have distinct phases across the waveguide band, this works as long as their reflection coefficients don’t coincide with one another. “Thru” connections (which are made by connecting the waveguide ports of the two test ports) can be used to extend SSL calibrations to two-port calibrations by applying SSL calibration to both test ports of the VNA. This produces a shortshort-load-thru calibration (or short open load-thru (SOLT) calibration) [38].

2.5 Terahertz VNA Calibration

43

Cable to port 1

Cable to port 1

Flush Short

Waveguide Adaptor

Waveguide Adaptor

Offset Short

Cable to port 1

Waveguide Adaptor

Load

Cable to port 1

Cable to port 2

Waveguide Adaptor

Line

Waveguide Adaptor

Fig. 2.7 Diagrammatic representation of various calibration standards for waveguide test ports

2.5.1.2

TRL/LRL Calibration

The thru-reflect-line (TRL) technique is a two-port calibration technique in waveguides [39]. Calibrations performed by TRL are highly accurate, often more accurate than those carried out by SOLT. This process is most commonly used when one requires a high level of accuracy and does not have calibration standards for that connector type. This is ideal for applications like rectangular waveguides, which are genderless interconnect environments. TRL calibration techniques typically involve using a waveguide line of approximately a λ/4 long at the mid-band frequency range of the waveguide. The “line” standard produces an adequate phase change across the entire waveguide band in comparison to the “thru” standard. As for the reflect standard is typically created using a flush-short, although other methods can be used (e.g., an offset-short). One limitation of the TRL calibration algorithm is that the “line” standard cannot have a signal phase-shift that differs by exactly m × π radians from the “thru” standard (where m is an integer). In this case, a short section of the waveguide is used to realize the standard line’s useful bandwidth. At the same time, this limitation presents a practical problem for sub-THz and THz systems, given that the physical length of the waveguide “lines” at 1 THz is typically around 100 μm [40, 41]. However, TRL calibrations can be implemented in millimeter and sub-THz frequency bands where the “line” standard waveguide sizes fall in a reasonable range (i.e., lengths are greater than 1 mm). The TRL calibration for sub-THz band waveguide dimensions requirements for various “line” standards (λ/4, 3 λ/4, 5 λ/4, and so on) are discussed in detail in [41]. The LRL (line-reflect-line) technique is sometimes used in place of the TRL technique (i.e., by using a second line instead of the thru) at millimeter and sub-THz

44

2 Electronic Sub-Terahertz VNA Measurement Techniques

Fig. 2.8 Distinction between the TRL and LRL calibration techniques

frequencies (i.e., above 30 GHz) [42]. All that is required now is that the phaseshift between the two line lengths must not differ by m x π radians, which allows longer lines to be used [43]. This method works well with a phase difference of approximately a quarter wavelength (90º) around the mid-band frequency of the waveguide. Figure 2.8 shows the distinction between the LRL and TRL Calibration techniques. The LRL technique can avoid the need to use very short lines at high frequencies (where the wavelength is relatively short, so a 1/4 wavelength line can be very short and damaged easily). As a result, the first-line standard of LRL should have well-known electrical properties (delay and loss), which is similar to the TRL calibration method from which LRL is derived. Both TRL and LRL use a short-circuit to connect to the reflect standard (normally a short-circuit). Although, in the LRL calibration technique, due to the unavailability of accurate prior knowledge of the first-line standard’s propagation characteristics, SOLT techniques are preferred at sub-THz and THz frequency ranges. The detailed assessments of TRL, LRL, thrushort-match (TSM), and SOLT calibrations in the THz region are performed in [44], while work in [45, 46] provides an alternate solution to the “line” standard problem in TRL calibration. Figure 2.9 shows an example of TRL and SOLT calibration kits provided by VDI technologies [27].

2.6 Sub-Terahertz VNA Measurement Systems for Permittivity Estimation

45

Fig. 2.9 TRL and SOLT calibration kits provided by VDI technologies

2.6 Sub-Terahertz VNA Measurement Systems for Permittivity Estimation In recent years, non-invasive, non-destructive, and contactless material characterization techniques have been a significant research domain due to their applicability in practical applications like biomedical imaging [47–49], the electronic industry, and food engineering. Biomedical imaging systems actively use the sub-THz spectrum due to its various advantages over other frequency bands like microwaves and Xrays [1–3]. As pointed out in Chap. 1, these frequency band imaging systems provide better resolution than microwave imaging systems. They are also less affected by the atmospheric conditions compared to the infrared. The sample size, state of the material, and dielectric properties of different dielectric materials necessitates the use of different material characterization methods. Coaxial probe, transmission line, free space, and resonant cavity are examples of conventional microwave frequency measurement techniques that can be used to calculate dielectric constant. While the resonant cavity method, for dielectric measurement, applies for narrowband frequency range, the transmission line method is material-specific and can only be used for characterizing machine able solids and liquids [50]. Both of these techniques require a small sample size in the sub-THz band. The use of split-ring resonators and metamaterials to measure the permittivity of polar liquids and dielectric analytes in the sub-THz frequency range has been reported in a few pieces of literature [51, 52]. The coaxial probe technique for material characterization works well for broadband frequency range but gives better results for liquids and semi-solids [53]. In this section, we will discuss the current dielectric characterization techniques in the sub-THz domain using VNA-based measurement setups.

46

2 Electronic Sub-Terahertz VNA Measurement Techniques

2.6.1 Nicolson-Ross-Weir Technique Nicolson and Ross proposed the algorithm for determining material dielectric properties based upon the S-parameters [54]. Their derivations of complex permeability and complex permittivity came from the electromagnetic signals of material reflections and transmissions. In later development by Weir [55], it became the widely used Nicolson-Ross-Weir (NRW) algorithm. The procedure of the NRW method for permittivity estimation is summarized in the block diagram in Fig. 2.10a [56]. The measurement setup consists of waveguide sections consisting of a sample holder of the same dimension in between, as shown in Fig. 2.10b. Prior to the measurement, 2-port VNA calibration is done to remove the systematic errors. S-parameters are obtained using the VNA attached to the waveguide sections, which are used to calculate the complex permittivity from the process described in Fig. 2.10a.

(b)

(a)

(c)

Fig. 2.10 a Flow chart of extraction of complex permittivity using NRW method. b Measurement setup consisting of waveguides and sample holders. c Length requirement of the sample holder [56]. Reprinted with permission from EuMA

2.6 Sub-Terahertz VNA Measurement Systems for Permittivity Estimation

47

Figure 2.10c shows the sample holder made of copper waveguide fitted in between the waveguide sections (Region 2). The inclusion of dielectric windows in the case of non-self-supporting samples is required to keep the sample contained. In the subTHz band, since the dimensions of waveguides are already small, mylar sheets are used to hold the biological samples under test in place as discussed in [57], which reports the complex permittivity of various biological tissues like blood, adipose tissue, calcified tissue, etc. The major disadvantage of this technique is the requirement of dimension-specific samples for accurate placement in the sample holder. In the sub-THz frequency range, the dimensions of the waveguide range from mm to μm, which demands a very small sample size. Hence, the NRW algorithm concepts are extended in the free-space region where there is no requirement for precise sample dimensions, which will be seen in the next section.

2.6.2 Quasi-Optical Free-Space Permittivity Measurement Free-space measurements provide a non-contact, non-invasive way to measure the reflection and transmission parameters [58]. When measuring scattering parameters, the sample should be parallel to the plane. An antenna and VNA are the main parts of the measurement setup. There are two aligned antennas, with one transmitting and the other receiving. The signal is incident on the material placed between two antennas and is captured by the other antenna after passing through the material. Therefore, it is possible to measure the free-space transmission coefficient. In reflection measurements, one antenna is coupled to the network analyzer using a directional coupler. Antennas transmit signals and measure reflections off the sample that is in front of them. The decrease in the output power strength of the transmitter in the sub-THz frequency range demands the requirement of focusing lenses in front of the horn antennas to concentrate the radiation at the samples’ surface and minimize diffraction effects [3, 59]. This results in the use of quasi-optical free-space material (QO-FSM) characterization in the sub-THz region.

2.6.2.1

Transmission-Based QO-FSM

Figure 2.11 shows the measurement setup of the QO-FSM technique for permittivity estimation [3]. Calibration of the measurement setup is done to prevent errors caused by the instrument system, cable, external environment, and other unwanted elements [3, 60]. There are two sets of calibrations performed in the QO-FSM method: (a) VNA related calibration (TRL, LRL, or SOLT as provided by the manufacturer), (b) gate-reflect-line (GRL) [61, 62] to prevent the errors arising due to antennas, air, and lenses which can cause signal attenuation. NRW algorithm in the free-space region is employed for the extraction of complex permittivity, which consists of four equations given by [63]:

48

2 Electronic Sub-Terahertz VNA Measurement Techniques

Fig. 2.11 Measurement setup of transmission based QO-FSM [3]

  1 − T2 −2γ0 L 1 S11 (ω) = .e 1 −  2 T2 

T 1 −  2 −γ0 (L 1 +L 2 ) S21 (ω) = .e 1 −  2 T2 T (ω) = e−ϒ L (ω) =

γ0 μ0 − γ μ0 γ0 μ0 + γ μ0

(2.7)

(2.8) (2.9) (2.10)

where  and T are the sample’s reflection and transmission coefficient; γ0 and γ are the propagation constants in free space and in the sample, respectively; L1 and L2 are the distances between the sample surfaces and the two antennas, and L represents the thickness of the sample. The terms e−2γ0 L 1 and e−γ0 (L 1 +L 2 ) are the attenuation factors in the air. A new generation of commercial material characterization kits (MCKs) (Fig. 2.12a), created by SWISSto12, is now available for waveguide frequency bands spanning the range of 50 GHz to 1.1 THz [64]. Essentially, this broadband strategy is a guided free-space approach using corrugated horn antennas used for complex permittivity estimation. While the material is being measured, it’s sandwiched between the two antennas that make up the MCK’s test ports, as illustrated in Fig. 2.12b. The use of huge parabolic mirrors or lenses is not required in these MCKs, as is the case with most QO-FSM setups, which significantly reduces the overall size of the measuring system. In [65–67], authors have recently reported the use of MCK kit for measurements of S-parameters for different low-loss dielectric materials, i.e., PTFE, Astra MT77, Rogers 3003, Alumina, Silicon, TPX, and HDPE. Authors in [65] have also analyzed the uncertainty in the measurement results due to the variation of thickness

2.6 Sub-Terahertz VNA Measurement Systems for Permittivity Estimation

49

(a)

(b) Fig. 2.12 a Compact sub-THz to THz material characterization kit (MCK) provided by SWISSto12. b Block diagram of guided wave free space permittivity estimation using corrugated horn (courtesy SWISSto12)

in the sample. It has been observed that systematic errors dominate the uncertainty in the extracted permittivity of the material.

2.6.2.2

Reflection-Based QO-FSM

Relatively new reflection-based QO-FSM requires only the complex reflection coefficient for permittivity estimation [68, 69]. In addition, there is no additional requirement of knowing the dielectric sample dimensions like other techniques, including resonant cavity, waveguide Nicolson-Ross-Weir (NRW) technique. Figure 2.13a shows the measurement setup for evaluating the complex permittivity of the flat sample [69]. In this method, the complex standing wave ratio (CSWR) is calculated using the S 11 parameter, which depends on the permittivity of the sample, as shown in Fig. 2.13b. Hence, the permittivity and loss tangent of the unknown lossy dielectric material can be extracted from CSWR using the following equations [69]: εr =

2[Re{CSWR( f )}]2 √ 1 + 1 + tan2 δ

(2.11)

50

2 Electronic Sub-Terahertz VNA Measurement Techniques

Fig. 2.13 a Measurement setup of reflection based QO-FSM. b Real and imaginary part of CSWR for lossy dielectric materials [69]. Reprinted with permission from IEEE



2tan arg{CSWR( f )}

tanδ = tan2 arg{CSWR( f )} − 1

(2.12)

where the complex permittivity is given by εr = εr − i εr = εr (1 − itanδ). There are two sets of calibrations required for acquiring S 11 parameters in this technique, i.e., the VNA calibration provided by the manufacturer (TRL, SOLT, or LRL) followed by quasi-optical calibration for shifting the reference plane at the first face of the sample [70]. This material characterization method works well for flat, sufficiently electrically thick samples (lossless and lossy) in the sub-THz band.

2.6.3 Open-Waveguide Probe for Viscous Liquid Permittivity Estimation Most of the biological samples like blood, tumors, and plasma are liquids or semisolid. The QO-FSM techniques are not suitable for the evaluation of the permittivity of these types of samples. There are various other methods for determining the electrical permittivity of liquids. Conventional waveguide measurements are commonly used with NRW algorithms containing liquid holders for permittivity estimation [71]. Other techniques include coaxial probes in microwave frequency range and resonant method [51]. But these methods require special liquid holders or suffer from poor accuracy in the sub-THz band.

2.6 Sub-Terahertz VNA Measurement Systems for Permittivity Estimation

51

Fig. 2.14 Multi-offset waveguide calibration technique for permittivity estimation [72]. Reprinted with permission from IEEE

Recently, authors in [72] came up with a simple measurement approach using the multi-offset calibration of the waveguide suitable for viscous liquid permittivity measurement. In the proposed methodology, by dipping an open-ended rectangular waveguide into the liquid sample and raising the liquid level by about λ/8 increments between measurements, four reflection measurements of the liquid sample are recorded in each material-filled waveguide. The proposed measurement setup is shown in Fig. 2.14, which illustrates the different positioning of waveguides at four consecutive locations inside the liquid at the increment of λ/8. VNA records the reflection coefficient by moving the waveguide probe upward by fixed increments ∼

of λ/8 for these four consecutive positions. The recorded reflection coefficient ( ) i

measurements are phase converted by adding phase delay of e jβ li , where β is the propagation constant of air-filled waveguide and l i represents the physical offset length of the liquid-filled waveguide as described in [72]. ∼

The  for four offset positions of the waveguide probe (l1, l 2 , l 3 , l 4 whereli = i

iλ ) 8

are used as input parameters in the following equation, which is used to obtain the propagation constant (γ): ⎡

∼

∼⎤

ρ1 1 ρ1  − 

1 1 ⎥⎡  ⎤ ⎢ ∼ ∼ ⎥ E1 ⎢ ⎢ ρ2 1 ρ2  −  ⎥⎢ ⎥ ⎢ 2 2 ⎥⎢ E 2 ⎥ ⎢ ∼ ∼ ⎥⎣  ⎦ = 0 ⎢ ρ3 1 ρ3  E3 −⎥ ⎥ ⎢ 3 3 ⎦ ⎣ 1 ∼ ∼ ρ4 1 ρ4  −  4

4

(2.13)

52

2 Electronic Sub-Terahertz VNA Measurement Techniques

Fig. 2.15 Experimental measurement setup for dielectric characterization of breast tissue phantom, including the placement of MUT on micro-positioner [74]. Reprinted with permission from IEEE

where, ρi = e−2γ li corresponds to the phase delay of ith offset load, and γ corresponds to the propagation constant of sample loaded waveguide. E 1 ’ , E 2 , E 3 represent the normalized error correction functions [73]. Finally, real (εr ) and imaginary (εr ) parts of complex permittivity of MUT are extracted by finding the iterative roots of propagation constant (γ ) using Newton Raphson Algorithm in MATLAB which is mathematically explained in Appendix A.1. The complex permittivity (εr − εr ) is related to the propagation constant by: εr

−

εr

   1 π 2 2 +γ = 2 ω μ0 ε0 a

(2.14)

This method has been recently used in the sub-THz domain for the permittivity estimation of breast tissue phantoms using Keysight VNA (N5247B) and frequency ∼

extender (Virginia Diodes, Inc., WR 6.5) for reflection coefficient ( ) measurement i

[74] (Fig. 2.15).

2.7 Conclusion In this chapter, we discussed the uses of sub-THz VNA for dielectric constant characterization in sensing and imaging applications. In contrast with microwave frequency VNA, sub-THz VNA also incorporates frequency extenders to upscale the frequency. A variety of frequency multiplication technologies are employed, most commonly Schottky diodes with harmonic multipliers. Our discussion covered the basic architecture of generic VNA and then extended it to THz VNA and how this system is used to evaluate the transmission and reflection coefficients of a device under test using S-parameters. In addition, we have discussed different calibration techniques

References

53

used for sub-THz VNA to remove errors that are caused by mismatch and leakage in the test setup, measurement signal path loss, internal reflections, system frequency response, and isolation between the reference and test signal paths. Following the calibration, different dielectric characterization methods can be used, such as NRW, quasi-optical free-space permittivity measurement, and open-waveguide probe techniques. These are discussed in the later sections. Permittivity measurements of solids and liquids require the integration of sub-THz probes, including horn antennas and waveguides, into the VNA system, which is also addressed in detail in this chapter.

References 1. Li C, Tofighi MR, Schreurs DMMP, Horng TSJ (2016) Principles and applications of RF/microwave in healthcare and biosensing. Academic Press, Princ Appl RF/Microwave Health Biosensing 2. Naftaly M, Shoaib N, Stokes D, Ridler NM (2016) Intercomparison of Terahertz dielectric measurements using vector network analyzer and time-domain spectrometer. J Infrared, Millimeter, Terahertz Waves 37(7):691–702 3. Hammler J, Gallant AJ, Balocco C (2016) Free-space permittivity measurement at terahertz frequencies with a vector network analyzer. IEEE Trans Terahertz Sci Technol 6(6):817–823 4. Ruan X, Chan CH (2019) Terahertz free-space dielectric property measurements using timeand frequency-domain setups. Int J RF Microw Comput Eng 29(9):21839 5. Sengupta K, Nagatsuma T, Mittleman DM (2018) Terahertz integrated electronic and hybrid electronic–photonic systems. Nat Electron 112(1):622–635 6. Goy P (1990) 8–1000 GHz vector network analyzer. In: International conference on infrared and millimeter waves. Int Soc Opt Photonics 1514:15140X 7. Goy P, Gross M (1997) Vector measurements from 8 Ghz to the thz range, obtained in a real life experiment. New Dir Terahertz Technol 323–340 8. Vector Network Analyzer—an overview | ScienceDirect Topics. https://www.sciencedirect. com/topics/computer-science/vector-network-analyzer 9. Horibe M, Kishikawa R (2012) Performance of new design of waveguide flange for measurements at frequencies from 800 GHz to 1.05 THz. In: 79th ARFTG microwave measurement conference: non-linear measurement systems, pp 1–6 10. Campion J, Shah U, Oberhammer J (2019) Silicon-micromachined waveguide calibration shims for terahertz frequencies. In: IEEE MTT-S international microwave symposium digest, pp 1265–1268 11. Weikle RM, Li H, Arsenovic A, Nadri S, Xie L, Bauwens MF, Alijabbari N, Barker NS, Lichtenberger AW (2017) Micromachined interfaces for metrology and packaging applications in the submillimeter-wave band. Addit Conf. https://doi.org/10.4071/2017dpc-tha3_present ation2 12. IEEE Microwave Theory and Techniques Society. Standards Coordinating Committee., Institute of Electrical and Electronics Engineers. IEEE standard for rectangular metallic waveguides and their interfaces for frequencies of 110 GHz and above. Part 1, Frequency bands and waveguide dimensions. IEEE Std 1785. 1–2012 13. Farago PS (1961) An introduction to linear network analysis. English Universities Press 14. Nikolova NK (2017) Introduction to microwave imaging. Cambridge University Press 15. Basu A (2014) An introduction to microwave measurements. CRC Press 16. Hui R, O’Sullivan M (2009) Basic instrumentation for optical measurement. Fiber Opt Meas Tech 129–258 17. Pennock SR, Abdul-Latif O (2017) Enhanced vector network analyzer time domain measurement using normalized superimposition. IEEE MTT-S Int Microw Symp Dig, 1255–1258

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18. Wu Z (2007) Software VNA and microwave network design and characterisation. John Wiley & Sons 19. Dunsmore JP (2012) Handbook of Microwave component measurements: with advanced VNA techniques 20. Deen MJ, Marinov O (2012) Measurement techniques and issues. Adv Imaging Electron Phys 174:1–117 21. Maestrini A, Thomas B, Wang H, Jung C, Treuttel J, Jin Y, Chattopadhyay G, Mehdi I, Beaudin G (2010) Schottky diode-based terahertz frequency multipliers and mixers. Comptes Rendus Phys 11:480–495 22. Hesler JL, Duan Y, Foley B, Crowe TW (2010) THz vector network analyzer measurements and calibration. In: 21st international symposium on space terahertz technology, pp 23–25 23. Hübers HW (2008) Terahertz heterodyne receivers. IEEE J Sel Top Quantum Electron 14:378– 391 24. Lin YJ, Jarrahi M (2020) Heterodyne terahertz detection through electronic and optoelectronic mixers. Reports Prog Phys 83(6):066101 25. Naftaly M (2015) Terahertz metrology. Artech House 26. Maestrini A, Ward J, Chattopadhyay G, Schlecht E, Mehdi I (2008) Terahertz sources based on frequency multiplication and their applications. Frequenz 62(5–6):118–122 27. VNA extension modules operational manual section 1-VNAX configurations, equipment and safety Pages 2–5. In: virginia diodes. https://www.vadiodes.com/en/products/vector-networkanalyzer-extension-modules 28. Dunsmore JP (2020) Handbook of microwave component measurements. John Wiley & Sons 29. Singh D, Salter MJ, Votsi H, Ridler NM (2020) Inter-laboratory comparison of S-parameter measurements with dynamic uncertainty evaluation. In: 94th ARFTG Microwave measure conference RF to millimeter-wave measure technology 5G beyond, pp 1–4 30. Collier R, Skinner D (2007) Microwave measurements. IET 31. Agilent Technologies (2006) Advanced calibration techniques for vector network analyzers. Mil Commun Radars 32. Rehnmark S (1974) On the calibration process of automatic network analyzer systems. IEEE Trans Microw Theory Tech 22(4):457–458 33. Fitzpatrick J (1978) Error models for systems measurement. Microw J 63–66 34. Bannister D, Griffin EJ, Hodgetts TE (1989) On the dimensional tolerances of rectangular waveguide for reflectrometry at millimetric wavelengths. National Physical Laboratory, Teddington, UK 35. Shoaib N (2016) Vector network analyzer (VNA) measurements and uncertainty assessment. Springer 36. How to perform a waveguide calibration. In: keysight Technol https://na.support.keysight.com/ materials/help/85071webhelp/85071How_to_Perform_a_Waveguide_Calibration.htm 37. Rumiantsev A, Ridler N (2008) VNA calibration. IEEE Microw Mag 9(3):86–99 38. Kruppa W, Sodomsky KF (1971) An explicit solution for the scattering parameters of a linear two-port measured with an imperfect test set. IEEE Trans Microw Theory Tech 19(1):122–123 39. Engen GF, Hoer CA (1979) Thru-reflect-line: an improved technique for calibrating the dual six-port automatic network analyzer. IEEE Trans Microw Theory Tech 27(12):987–993 40. Naftaly M, Clarke RG, Humphreys DA, Ridler NM (2017) Metrology state-of-the-art and challenges in broadband phase-sensitive terahertz measurements. Proc IEEE 105(6):1151–1165 41. Ridler NM (2009) Choosing line lengths for calibrating waveguide vector network analysers at millimetre and sub-millimetre wavelengths. Natl Phys Lab 42. Hoer CA, Engen GF (1987) On-line accuracy assessment for the dual six-port ANA: extension to nonmating connectors. IEEE Trans Instrum Meas 2:524–529 43. Fuh KF (2016) Formulation for propagation factor extractions in thru-reflect-line/line-reflectline calibrations and related applications. IEEE Trans Microw Theory Tech 64(5):1594–1606 44. Williams DF (2011) 500 GHz-750 GHz rectangular-waveguide vector-network-analyzer calibrations. IEEE Trans Terahertz Sci Technol 1:364–377

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45. Williams DF (2010) Rectangular-waveguide vector-network-analyzer calibrations with imperfect test ports. In: 76th ARFTG microwe measure conference millimeter-wave meas model ARFTG 2010, pp 1–8 46. Ridler NM, Clarke RG (2016) Establishing traceability to the international system of units for scattering parameter measurements from 750 GHz to 1.1 THz. IEEE Trans Terahertz Sci Technol 6:2–11 47. Li C, Un KF, Mak PI, Chen Y, Munoz-Ferreras JM, Yang Z, Gomez-Garcia R (2018) Overview of recent development on wireless sensing circuits and systems for healthcare and biomedical applications. IEEE J Emerg Sel Top Circuits Syst 8(2):165–177 48. Mirbeik-Sabzevari A, Tavassolian N (2019) Ultrawideband, stable normal and cancer skin tissue phantoms for millimeter-wave skin cancer imaging. IEEE Trans Biomed Eng 66(1):176– 186 49. Yaduvanshi RS, Nishtha M (2021) Conical dielectric resonator antenna for Terahertz applications. Frequenz 75(5–6):211–220 50. Works CN, Dakin TW, Boggs FW (2013) A resonant-cavity method for measuring dielectric properties at ultrahigh frequencies. Electr Eng 63(12):1092–1098 51. Park SJ, Cunningham J (2020) Determination of permittivity of dielectric analytes in the Terahertz frequency range using split ring resonator elements integrated with on-chip waveguide. Sensors (Switzerland) 20(15):4264 52. Park SJ, Yoon SAN, Ahn YH (2016) Dielectric constant measurements of thin films and liquids using Terahertz metamaterials. RSC Adv 6(73):69381–69386 53. Chabbra M, Fan SQ, Misra D (1989) Study on the open-ended coaxial line method for measuring the permittivity of materials at microwave frequencies. In: 6th IEEE conference record, instrumentation and measurement technology conference, pp 541–545 54. Nicolson AM, Ross GF (1970) Measurement of the intrinsic properties of materials by timedomain techniques. IEEE Trans Instrum Meas 19(4):377–382 55. Weir WB (1974) Automatic measurement of complex dielectric constant and permeability at microwave frequencies. Proc IEEE 62(1):33–36 56. Ng SK, Noh B, Williams K, Gibson A, Haigh A, Parkinson G, Ainsworth P, Plunkett A (2008) An automated microwave waveguide measurement technique. In: Proceeding 38th Europe microwave conference, pp 1322–1325 57. Shimonov G, Koren A, Sivek G, Socher E (2018) Electromagnetic property characterization of biological tissues at D-Band. IEEE Trans Terahertz Sci Technol 8(2):155–160 58. Fenner RA, Rothwell EJ, Frasch LL (2012) A comprehensive analysis of free-space and guidedwave techniques for extracting the permeability and permittivity of materials using reflectiononly measurements. Radio Sci 47(01):1–13 59. Friedsam GL, Biebl EM (1997) Broadband free-space dielectric properties measurement system at millimeter wavelengths. IEEE Trans Instrum Meas 46(2):515–518 60. Zhang X, Chang T, Cui HL, Sun Z, Yang C, Yang X, Liu L, Fan W (2017) A free-space measurement technique of Terahertz dielectric properties. J Infrared, Millimeter, Terahertz Waves 38(3):356–365 61. Bartley PG, Begley SB (2012) A new free-space calibration technique for materials measurement. In: 2012 IEEE I2MTC—international instrumentation and measurement technology conference, proceedings, pp 47–51 62. Chang T, Zhang X, Yang C, Sun Z, Cui HL (2017) Measurement of complex Terahertz dielectric properties of polymers using an improved free-space technique. Meas Sci Technol 28(4):045002 63. Boughriet AH, Legrand C, Chapoton A (1997) Noniterative stable transmission/reflection method for low-loss material complex permittivity determination. IEEE Trans Microw Theory Tech 45(1):52–57 64. Dimitriadis AI (2020) Dielectric measurements at mm-wave frequencies with the material characterization kit (MCK). In: Towards Terahertz technology for high throughput communications. Polydome EPFL, Lausanne

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65. Wang Y, Shang X, Ridler NM, Huang T, Wu W (2020) Characterization of dielectric materials at WR-15 Band (50–75 GHz) using VNA-based technique. IEEE Trans Instrum Meas 69:4930– 4939 66. Khalid A, Cumming D, Clarke R, Li C, Ridler N (2017) Evaluation of a VNA-based material characterization kit at frequencies from 0.75–1.1 THz. In: Proceedings of IEEE 9th UK-EuropeChina work millimetre waves Terahertz technology UCMMT, pp 31–34 67. Ma M, Wang Y, Navarro-Cía M, Liu F, Zhang F, Liu Z, Li Y, Hanham SM, Hao Z (2019) The dielectric properties of some ceramic substrate materials at Terahertz frequencies. J Eur Ceram Soc 39:4424–4428 68. Yashchyshyn Y, Godziszewski K (2019) New opportunities in quasi-optical materials characterization in far infrared region. Int Conf Transp Opt Networks 1–4 69. Yashchyshyn Y, Godziszewski K (2018) A new method for dielectric characterization in subTHz frequency range. IEEE Trans Terahertz Sci Technol 8(1):19–26 70. Bourreau D, Péden A, Le Maguer S (2006) A quasi-optical free-space measurement setup without time-domain gating for material characterization in the W-band. IEEE Trans Instrum Meas 55(6):2022–2028 71. Wang Y, Afsar MN (2003) Measurement of complex permittivity of liquids using waveguide techniques. Prog Electromagn Res 42:131–142 72. Sahin S, Nahar NK, Sertel K (2020) Waveguide probe calibration method for permittivity and loss characterization of viscous materials. In: 2020 94th ARFTG microwave measurement conference: RF to millimeter-wave measurement techniques for 5G and beyond, pp 1–3 73. Lewandowski A, Wiatr W, Gu D, Orloff ND, Booth J (2017) A Multireflect-thru method of vector network analyzer calibration. IEEE Trans Microw Theory Tech 65(3):905–915 74. Kaurav P, Koul S, Basu A (2021) Electromagnetic characterization of breast tissue phantoms in D band regime. IEEE J Electromagn RF Microwaves Med Biol. https://doi.org/10.1109/jerm. 2021.3078645

Chapter 3

Biological Tissue Interaction with Sub-Terahertz Wave

Abstract Non-invasive probing of biological molecules, cells, and tissues without tagging are one of the most significant scientific challenges of the present. The study of the dielectric properties of biological systems and their components is a mature field of research that has steadily evolved for almost a century. In the sub-THz/THz frequency bands, THz spectroscopy has been studied to probe these dielectric properties of biological systems. With this technology, researchers can rapidly and accurately acquire permittivity and conductivity spectra and derive realistic electrical models of cells and organelles non-invasively, thus overcoming the above-stated challenge. The previous chapter dealt with the utilization of various electronic sub-THz systems for determining dielectric permittivity of liquids and solids using VNA-based setups. In this chapter, we describe the dielectric properties of biological tissues and liquids in the sub-THz range. We will learn how to develop a model of water and water-containing media dielectric permittivity, from which we can analyze relaxation mode and damped resonant modes which play an important role in understanding the dielectric mechanism inside the cells on application of electric fields. We will also analyze the dielectric models of biological solutions using effective medium theory and volume fraction analysis.

3.1 Introduction During the 1980s, when the development of sub-THz/THz technologies began, their use in the medical-biological area became one of their widespread applications. It is the specific response of biological systems to these frequencies’ radiation that explains their high informativeness. In particular, sub-THz and THz waves are being investigated for their diagnostic and imaging potential [1–5]. Apart from these, other potential applications are emerging, such as the characterization of DNA and other proteins by (labeled-free) biosensors and viability studies of living cells [6–8]. From the beginning, it was evident that water is the principal feature of the information and the main obstacle in the sub-THz/THz region [9–11]. As a result of its intense absorption in this frequency range, on the one hand, it is less likely to penetrate © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. K. Koul and P. Kaurav, Sub-Terahertz Sensing Technology for Biomedical Applications, Biological and Medical Physics, Biomedical Engineering, https://doi.org/10.1007/978-981-19-3140-6_3

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biological tissues. Yet, on the other hand, it may also be a fruitful subject for further investigation. In biological systems, hydrogen bonds, which are the basis of all life, are comparable to THz quantum energy [12, 13]. It can therefore be considered that the water molecular itself is a universal THz marker that reflects the functions of various tissues and cells within living tissues. The bulk or bound form of water is the most important substance in the tissues of our bodies [14]. In the years following the introduction of Debye’s relaxation theory of dielectrics in 1929, many researchers have explored the dielectric response of water [15–18]. Researchers have identified two relaxation properties for the dielectric spectrum of water where the slow relaxation occurs in the microwave to the millimeter-wave range. In contrast, the fast relaxation occurs after 0.1 THz. Water’s hydrogen-bond networks are linked to fast relaxation water. There is little information about the molecular mechanisms that control such fast relaxation behavior [19]. The majority of biological tissues are made up of water, including body fat, some hard tissues, like teeth, and up to 85% of soft tissues, including lungs and muscles [19]. On average, water concentration varies from 83% in the lungs, 68% in the skin, muscles, and kidneys, to only 5% in teeth [20]. A thorough understanding of tissue water behavior in the sub-THz and THz range is necessary because radiation absorbs strongly, and malignant neoplasms are sensitive to metabolic hydration. This chapter establishes the fundamentals of dielectric theory in terms of subTHz/THz applications in medicine. It does so from a perspective that would be of value to engineers and scientists working on developing sub-THz/THz biosensors for medical applications. Following this, discussion of modeling macroscopic properties (bulk complex permittivity) is presented. Several water dielectric permittivity models are presented in Sect. 3.3, extending them to the THz frequency range. In Sects. 3.4 and 3.5, we will analyze dielectric models of biological solutions using effective medium theory and volume fraction analysis.

3.2 Dielectric Spectroscopy Spectroscopy is the study of molecular or atomic structure and composition of materials by measuring the EM radiation absorbed, emitted, or scattered by and within the material in response to its wavelength. As long ago as the nineteenth century, spectroscopy was first used to study the wavelength dependence of the absorption of visible light by gas-phase matter [21]. Quantum mechanics was developed from spectroscopic studies, such as Max Planck’s account of blackbody radiation, Albert Einstein’s explanation of the photoelectric effect, and Niel Bohr’s account of atomic structure and spectrum [22]. The unique spectra of atoms and molecules make spectroscopy an important tool in physical and analytical chemistry. This makes these spectra useful for detecting, identifying, and quantifying atomic, molecular, and energetic information [23–26]. Biomedical spectroscopy has important applications in tissue analysis and medical imaging in the biomedical field.

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Fig. 3.1 Interaction of different parts of EM spectrum with matter [27]. HyperPhysics ©C.R. Nave, 2017

Figure 3.1 illustrates the range of interactions EM radiations with materials at different wavelengths along the electromagnetic spectrum, including larger molecular vibrations at smaller frequencies, such as microwaves, to photoionization at very high frequencies such as X-rays [27]. EM wave interactions can be understood at frequencies below approximately 6 THz using classical dielectric wave parameters. Transitions between molecular vibrational and rotational energy levels become increasingly necessary at higher frequencies and are more easily understood when viewed through a quantum–mechanical framework [28]. Dielectric spectroscopy is a widely used technique to investigate the macroscopic response when subjected to an applied electric field with a fixed or changing frequency [29–31]. Understanding the dielectric parameters of biological materials is essential for quantifying the interaction between EM fields and these materials. Hence, this section will cover the basics of dielectric material characterization: dielectric spectroscopy parameters, dielectric mechanism variation with respect to frequency, and mathematical modeling of complex permittivity using Debye and Cole–Cole dielectric models.

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3.2.1 Dielectric Polarization In dielectric materials, the charges are bound with molecules and atoms and therefore do not move over macroscopic distance under the influence of an applied electric field [32]. The electron clouds associated with the molecules and atoms are instead distorted, realigned, or repositioned. The result is the polarization of materials and the formation of electric dipoles. Polarizability varies with dielectric substrate types. Different dielectric mechanisms are associated with how a studied medium reacts to an applied electrical field. The characteristic frequency of their response defines these mechanisms. The most common dielectric mechanisms depending on resonance and relaxation processes are as follows [33–35]: Electronic Polarization: When a nonpolar material is exposed to an electric field, the electrons move away from the nucleus according to the intensity of the electric field. This leads to small electric dipoles aligning themselves in accordance with the electric field, as shown in Fig. 3.2a. The electron returns to its original state after it is removed from the external field. Ionic (Molecular) Polarization: The polarization of dissolved ions determines ionic polarization. For example, NaCl dissolved in water exists as individual Na+ and Cl− ions, not as neutral ions. The polar molecules of water tend to maintain a Fig. 3.2 Polarization mechanism in dielectric

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neutral polarity by bonding with the positive Na+ ions in the absence of an electric field. The solution of NaCl becomes net polarizable when an external electric field is applied. The polar water molecules dissociate from Na+ and Cl− ions and align themselves to the electric fields. Figure 3.2b shows the ionic polarization after the application of electric field. Orientational (Dipole) Polarization: Materials with polar molecules, such as water, exhibit this type of polarization. Due to its polar molecules, water has a permanent dipole moment. In an ideal state, all molecules will follow random orientations due to thermal agitation. Hence, they do not exhibit net polarization, as shown in Fig. 3.2c. The polar molecules in the material tend to align themselves with the applied electric field when the material is exposed to an electric field. As a result, the material becomes net polarizable.

3.2.2 Dielectric Spectroscopy Parameter: Complex Permittivity An electric field induces polarization within a material whenever it is applied to the material. Increased electrical fields lead to increased polarization in the case of linear isotropic material [36]. This increment is linear and is expressed in terms of a constant called permittivity. Permittivity ε is essentially defined by the ability of a material to polarize in response to an electric field or the resistance a material encounters when a field is applied. In electromagnetics, permittivity is usually discussed in terms of relative permittivity. Relative permittivity (εr ) is the ratio of the material’s permittivity to the vacuum’s permittivity, which is expressed as follows: εr =

ε ε0

(3.1)

where ε0 = 8.854 × 10−12 . Sometimes εr is also referred to as dielectric constant. When the source is direct current, the above equation is true. However, an additional loss term is required for time-varying electric fields, which is represented by a complex number. As a result, the permittivity representation becomes: ε = ε − i ε

(3.2)

where ε and ε represent the real and imaginary part of permittivity. The real permittivity of a material indicates how much energy is stored, and the imaginary permittivity accounts for how much energy is absorbed. We often refer to ε as the “loss factor”. “Loss tangent” (tanδ) is another term used to express the level of absorption of a material, which is the ratio of imaginary permittivity to real permittivity, given by:

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tan δ =

ε ε

(3.3)

A higher tanδ indicates a higher absorption of energy for the applied electric field. If a material exhibits conduction properties due to the applied field, the conduction term is defined by a quantity σ. σ = ωε0 εr

(3.4)

where ω represents the angular frequency. The interaction of EM fields with the material depends on the dielectric parameters of those materials, from solids to liquids and plasmas [37–39]. These dielectric parameters are characterized using dielectric spectroscopy. This technique obtains the dielectric characteristics of a material that are described as a function of frequency. The refractive index is another parameter that is most used in dielectric spectroscopy. In Appendix A.2, the relationship between dielectric permittivity and refractive index is derived.

3.2.3 Frequency Response of Dielectric Mechanisms The permittivity of a material decreases with increasing frequency. The relationship between a material’s permittivity and frequency is shown in Fig. 3.3. It is observed that the dielectric mechanism has its own “cut-off frequency” along with the dominant mechanism in sub-THz/THz regions. In response to increasing frequency, slow mechanisms drop away, leaving the fast mechanisms to contribute to εr . At each critical frequency,εr will peak correspondingly. Depending on the material, the magnitude and “cut-off frequency” of each mechanism varies. Although water has a strong dielectric effect at low frequencies, its dielectric constant drops dramatically around Fig. 3.3 Dielectric mechanism response with frequency curve showing real and imaginary part of permittivity

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22 GHz. Conversely, Teflon lacks dipolar mechanisms, so its permittivity remains virtually constant even in the millimeter-wave region. An electronic or ionic polarization can create a resonance effect, while a dipolar polarization can create a relaxation effect [40].

3.2.4 Relaxation Theory Dipoles try to align in the applied field direction; as mentioned earlier, dipole alignment changes as the field direction reverses. The time taken by the charges to adjust to the changing field is known as relaxation time (τ ). Due to the increased frequency, the charges are given less time to align and eventually fail to keep up. This frequency is known as the relaxation frequency. In addition, we can say that polarization lags the applied field, as demonstrated by Debye [37]. Debye also showed the dependence of permittivity on frequency (f ) and relaxation time (τ ) as follows [37, 41]: ε = ε − iε = ε∞ +

εs − ε∞ 1 + iωτ

(3.5)

where εs and ε∞ are dielectric constants at D.C (static) and optical frequencies, respectively. As a result of separating the real (ε ) and imaginary parts (ε ) of permittivity, we get: ε = ε∞ + ε =

εs − ε∞ 1 + (ωτ )2

(εs − ε∞ )ωτ 1 + (ωτ )2

(3.6) (3.7)

Equation (3.5) takes only one type of polarization mechanism into account. Generally, each polarization mechanism will have its own relaxation time, which will be added to Eq. (3.5) to give [42]: ε = ε − i ε = ε∞ +

εs − ε∞ εs − ε∞ εs − ε∞ + + 1 + i ωτd 1 + iωτi 1 + i ωτe

(3.8)

where τd , τi , τe represent the relaxation times for dipole, ionic and electronic polarization, respectively. Sometimes, the Debye model does not provide the appropriate dielectric constant. Several studies have shown that relaxation phenomena occur at much higher frequencies than what the model predicts. With the addition of one term α to the Debye model, Cole and Cole have taken into account the relaxation phenomenon over wider bandwidths [43], giving:

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ε = ε − i ε = ε∞ +

εs − ε∞ 1 + (i ωτ )1−α

(3.9)

the α term extends the relaxation to the much wider bandwidth. The frequency range from 0.1 to 3 THz corresponds to a relaxation time range from 0.15 to 10 ps. Three types of relaxation can be studied using the sub-THz/THz frequency range [44]: bulk solvent relaxation, which occurs in picosecond time scales; large-angle rotations of unbound molecules on picosecond time scales; small molecular movements (less than a molecular diameter) that occur on time scales of femtoseconds.

3.3 Dielectric Characterization of Water in Sub-Terahertz/Terahertz Regime Almost all tissues and fluids in the body are made of water. Since the relaxation times of water molecules correspond to the THz and sub-THz frequency bands, it can be used to diagnose relative changes in water molecular concentrations and properties. THz spectroscopy is used to quantify the state of the water itself that is present in the samples [45]. A water molecule can be differentiated into bulk water which does not form bonds with biomolecules, and hydration or bound water which surrounds and interacts with biomolecules [46, 47]. Bound water is tightly bound to tissue’s molecular components and cannot be moved except during tissue compression or heating. Tissue water is composed mainly of free water and is present in interstitial fluid and cell cytoplasm [48]. It can move from one space to another or even outside, responding to either osmotic or mechanical pressure. The sub-THz/THz spectrum is sensitive to the change in proportions of free and bound water and the relaxation time for each state of water [49]. The THz region of water is highly absorbing due to its polar nature. Hydrogen atoms are covalently bonded to oxygen atoms in water to form a simple molecular structure. The oxygen molecule possesses four electrons, of which two bond with hydrogen and two remain unshared. This makes oxygen atoms “electronegative” compared to hydrogen molecules. Because of this uneven electron distribution, water molecules are polar. A water network is constructed through hydrogen bonds (HBs) between molecules of water [50]. This network has a tetrahedral-like structure. Water is unique among all liquids because of the high number of hydrogen bonding sites and the equal number of proton donors and acceptors [51, 52]. The water surrounding polar groups is thought to have three-dimensional coordination distortions, in addition to reorientation being dynamically retarded [53, 54]. The importance of water molecules perturbed by polar groups (hydrated water) has frequently been expressed throughout biology. Their structural characteristics and dynamical properties differ from bulk water.

3.3 Dielectric Characterization of Water …

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3.3.1 Relaxation Models of Water Dielectric spectroscopy using THz is a reliable technique for probing the intermolecular dynamics of water. Solvents (polar liquids) are of particular interest in THz spectroscopy because they exhibit very strong absorption and dispersion [55–57]. With the help of dielectric models in a wide spectral range, one can determine the appearance and structure of hydration shells around the solute molecules. We will discuss some of the dielectric models that have been developed in the sub-THz/THz regime in recent years.

3.3.1.1

Modified Debye Model

Typically, a Debye model of two components has been used as the basis for the description of data obtained from THz spectroscopy [58, 59]. In the double Debye model, the complex permittivity is described by two relaxation time terms. In the THz frequency range, the water response has been found to exhibit two relaxation processes attributed to the breakdown of hydrogen bonds within the tetrahedral molecular arrangement of water and its reorientation. A relaxation time of approximately 8.5 ps is believed to illustrate the breaking of bonds, and a relaxation time of approximately 0.15 ps is believed to illustrate the reorientation of the molecule. The details of the two-component Debye model for water in the THz region can be found in [60, 61]. Even the two-component Debye formula does not always explain the experimental data correctly with increasing measurement accuracy [45]. Several modifications to the Debye model that consider the deviations from the dielectric spectrum of the solution have been achieved for the sub-THz frequency range. A formula containing the term i ωτ using the Cole–Cole model, taking into account the broadening of the permittivity spectrum observed when sugars and proteins dissolve in water [53, 62]. Raicu et al. in [45] presented a generalized formula for dielectric function spectrum of water in modified Debye-type model using relaxation (εrelax ) and over-dumped oscillator (εosc ) components from MW to THz range given by: ε(ω) = ε∞ + εosc + εrelax + εcond

(3.10)

where ε∞ is the high-frequency limit of complex dielectric permittivity, and the σ describes the losses due to the ionic conductivity σ. Following last term εcond = i ωε 0 two relaxation terms in complex permittivity (for the “slow” γ-relaxation process and “fast” Debye terms), Eq. (3.10) can be written as [49]: ε1 γ 1 + (i ωτ1 )α

ε(ω) = ε∞ +  +

iσ ε2 A1 + + ··· + 2 2 1 + (i ωτ2 ) ω01 − ω + iγ01 ω ωε0

(3.11)

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Fig. 3.4 Complex dielectric spectrum of water at 22 °C obtained from [63]. The real and imaginary parts of permittivity are obtained using microwave spectroscopy in the MMW region [65, 66], while the red curve is the obtained dielectric constant using TDS [63]. The highlighted portion represents the sub-THz region. (Reprinted with permission from [63] © 2009 Elsevier B.V. All rights reserved.)

where τ1 and τ2 are the relaxation times for the first (the main “slow” γ-relaxation process) and second (“fast”) Debye terms; ε1 and ε2 are the contributions to the permittivity of the first and second Debye terms, respectively; A1 is amplitude corresponding to frequency ω01 of the overdamped oscillator; γ01 is the Lorentz term linewidth. Equation (3.11) holds true for the following condition [49]: εs = ε∞ + ε1 + ε2 +

A1 + ··· 2 ω01

(3.12)

Figure 3.4 illustrates the complex dielectric spectrum of water at 22 °C obtained from [63]. The physical mechanism involved in Eq. (3.11) can be explained as follows [19, 64]: 1.

2.

3.

The slow relaxation mode ε1 ) located around 20 GHz is a large amplitude mode. In this process, hydrogen bonds (HB) between bulk water molecules are switched through a cooperative mechanism [67]. There are two possible interpretations of the fast relaxation mode (ε2 ) around 0.6 THz: (a) A dynamical scenario is assumed to exist, which assumes an intrawell relaxation during the waiting period of ε1 or a global HB network fluctuation. (b) Another structural scenario assumes that the water has two fractions, where a part of it is non-hydrogen-bonded (NHB) water disconnected from the HB network, and ε2 is assigned to collisional relaxation of the NHB water. Pure water exhibits these Debye-type relaxation modes in the microwave and sub-THz range [68]. 1 above Water exhibits intermolecular vibration/oscillation modes ω2 −ωA2 +iγ 01 ω 01 1 THz. Water’s spherical stretching (V s ) (~5 THz) and its rotation liberation

3.3 Dielectric Characterization of Water …

67

(V l ) (>12 THz) (see Fig. 3.4) can be attributed to hindered O···O translation and hindered O···H rotation, respectively. The details of these modes for 3 THz and above can be found in [63, 69]. Table 3.1 summarizes the values of these parameters from various reported works, including [15, 49, 59, 61, 70] using different spectroscopy methods like TDS and ATR (Attenuated Total internal Reflection) for pure water at different temperatures in the sub-THz region. It should be noted that the complex dielectric constant is also a function of temperature. Ellison has well documented the permittivity of pure water over 0–25 THz frequency range for the temperature range of 0–100 °C in his work in [71] where it is shown that changes in the temperature have a noticeable effect on the frequency band of MW and sub-THz band due to change in slow relaxation time τ1 . Figure 3.5 shows the temperature dependence on absorption coefficient, refractive index, and complex permittivity of liquid water in sub-THz and THz frequency range as studied in [72].

3.3.1.2

Defect Migration Model

Based on Debye’s theory, the main source of dielectric relaxation is rotational diffusion in a viscous continuum involving spherical polar molecules [45]. While the modified Debye model yields the correct value for the dielectric time relaxation τ1 = 4πa3η/k B T (a: radius of the sphere, T: absolute temperature; k B : Boltzmann constant; η: viscosity), it cannot be applied to strongly associated liquids. Rather than rotating in a viscous continuum, the water molecule rotates within an open Hbond network [67]. Thus, rather than continuous diffusion, water’s dipole rotation is assumed to reflect switching between dipole directions. In recent years, a wait-and-switch relaxation model has been gaining popularity among bulk water relaxation models [67, 73, 74]. Water molecules reorient at larger angles only because of defects in the H-bond network; otherwise, they remain in a waiting mode. The waiting regime results in a relaxation delay. In other words, the migration of defects through the H-bond network causes the change in total polarization. There are two types of defects in this model: orientation and ionic. In orientation defects, the thermal fluctuations in water molecules lead to distortion of the tetrahedral structure of water, leading to bifurcated/trifurcated H- bonds [75]. Popov et al. describe the bifurcation and trifurcation of H-bonds with the help of an example in [67]. The presence of orientation defect leads to reorientation of water molecules which results in a change in the dipole moments direction in the consecutive molecules. The details of this mechanism are described in [76, 77]. The experimental demonstration of this defect is elaborated in [78, 79]. In ionic defects, the migration of H3 O+ and OH− leads to the polarization of water [80]. This mechanism does not involve rotation of the whole molecule, like in an orientation defect mechanism. Water molecules change their dipole tip direction when the charges of their protons rearrange due to proton hopping [81].

3.68

3.20

3.48

–

79.48

78.8

78.36

78.4

0.1–2

0.1–2.0

0.05–1.5

0.18–2.42

ε∞

2.45

εs

77.5

Frequency (THz)

0.08–2.7

–

73.43

73.60

74.39

73.00

ε1

8.25

8.24

8.00

12.3

9.50

τ 1 (ps)

Table 3.1 Parameters of the modified Debye model of water ε2

τ 2 (fs)

180 310 ± 60

1.4 ± 0.1

180

110

360

1.45

1.60

1.42

1.70

ω01 (THz)

5.40

–

–

–

5.30

As/ω01

1.57

–

0

–

1.12

γ 01 (THz)

–

–

–

–

5.35

T (°C)

25

22

22

22

22

)References

[70]

[61]

[59]

[15]

[49]

68 3 Biological Tissue Interaction with Sub-Terahertz Wave

3.3 Dielectric Characterization of Water …

69

Fig. 3.5 a Power absorption and refractive index, b complex permittivity of liquid water as a function of frequency for different temperatures (Reprinted with permission from [72] Copyright 2013, American Institute of Physics.)

Using this idea of orientation/ionic defects migration, Popov et al. came up with the mean square displacement term of the defects, and the complex permittivity (ε(ω)) was given as [67]: ε(ω) = ε∞ +

ε −1 2  1 + 6ki ωnq r ∗2 (ω) B T ε0 

(3.13)

  where q and n are the effective charges, number density; r ∗2 (ω) : Fourier transform of the mean square displacement of the defect; ε0 is the permittivity  of vacuum,  and kB is the Boltzmann constant. Under normal diffusion condition r ∗2 (ω) can be related to the diffusion coefficient of defect migration(Ddefect ) as  2  r (t) = 6Ddefect t

(3.14)

Substituting (3.14) in (3.13) we get, ε(ω) = ε∞ +

k B T ε0 ε , τdefect = 1 + j ωτdefect nq 2 Ddefect

(3.15)

Equation (3.15) is applicable for the MW frequency range. Vibrations of the H-bond network become significant in the sub-THz band. The vibrational motion

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3 Biological Tissue Interaction with Sub-Terahertz Wave

between each defect in the water molecule is an essential part of defect migration. Thus, the modified equation of complex permittivity includes the superposition of translation and oscillation motions given as: ε(ω) = ε∞ +



1 + ( j ωτdefect )

ε −1

+ ( j ωτosc )−δ

−1

(3.16)

where τosc corresponds to relaxation time due to the vibration motions δ is related to the spectral dimension of the H-bond network. The details of estimation of these terms can be found in [67, 82, 83]. In MW, the imaginary part of permittivity doesn’t change with change in δ, while in the sub-THz/THz band, it decreases with an increase in δ for a particular frequency [67]. It is evident that the specific behavior of function Eq. (3.16) around the peak of the losses allows us to describe the non-Debye behavior of the relaxation of water in the sub-THz region. Unlike the two-fraction water models discussed in the previous section, we won’t need to introduce an additional “fast” Debye process here. The high-frequency (>10 THz) includes the other oscillation losses, which are discussed in detail in [67].

3.4 Dielectric Characterization of Biological Solution Using Terahertz Hydration In many biological processes, water molecules confined to microcavities at interfaces (Hydration) play a crucial role due to their hydrogen bonding properties, which differ from those of bulk water [84–86]. Additional interactions occur between water molecules on protein or lipid surfaces or within cells and tissues. In biological environments, water molecules that exhibit unique properties are often referred to as “biological water.” Fig. 3.6 illustrates the water solution model, consisting of solute surrounded by a solvation shell (hydrated water) and bulk water [87]. The characteristics of biological water differ from those of bulk water. Hydrogen bonding between water molecules causes mutual polarization of molecules, resulting in a bigger dipole moment and dielectric constant for bulk water [88, 89]. This polarization does not exist for water molecules in biological molecules, i.e., biological water has a lower polarity than bulk water. The presence of around 10–25% of water molecules in cells has been measured to have slower reorientation dynamics compared to bulk water. Molecular water molecules form a tetrahedral cluster constituted by hydrogen bonds fluctuating in frequency, showing relaxational behaviors with a lifetime of several picoseconds [90]. The equilibrium of hydration in water is determined by working from both enthalpic and entropic thermodynamics, including the enthalpies of hydrogen bonding and electrostatic interactions and that of breaking down the relatively ordered hydrogen bonds of bulk water resulting in new hydrogen bonds tailored for the geometric factors of biological interfaces [91, 92].

3.4 Dielectric Characterization of Biological Solution …

71

Fig. 3.6 Model of biological water solution consisting of solute surrounded by solvation shell (hydrated water) and bulk water. Reprinted with permission from [87], Copyright © 2017, American Chemical Society

In solution, water hydration has been shown to slow down the relaxational motion to nanoseconds or microsecond timescales. Figure 3.7 represents the dielectric loss spectra of solute and hydrated water molecules demonstrating different dielectric losses for the solute, bulk, and hydrated water [93]. It is challenging to determine the properties of hydrated water from low-frequency dielectric responses since water and solute molecules have complicated relaxational responses in the MW region. Dielectric losses resulting from the relaxational motion of bulk water are reduced by using hydrated water in the sub-THz/THz frequency region. Hydrated water can be characterized quantitatively by evaluating the decrement of the dielectric loss as well as the change in the real part of the dielectric constant. Hence, by using the complex dielectric function, THz spectroscopy can provide direct information on the picosecond dynamics of water molecules [94, 95].

Fig. 3.7 Dielectric loss spectra of solute, hydrated water, and bulk water. In a solution containing only bulk water, the dielectric loss would be like that indicated by the dotted line. A difference between the solid and dotted lines in the THz frequency region indicates hydrated water. Reprinted with permission from [93], Copyright © 2008, Elsevier

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3 Biological Tissue Interaction with Sub-Terahertz Wave

Attempts have been made to characterize this hydration state of water molecules in the THz frequency range using complex dielectric functions using solvated saccharides in [49, 53, 93, 96]. It has been shown that the dielectric spectral response of hydrated water solutions takes all three components into account (solute, bound water, and free water) [19]. Hence the total absorption coefficient (αtotal ) of the solution with volume (Vtotal ) is given by [96]:  αtotal = αsolute

Vsolute Vtotal



 + αhydration

Vhydration Vtotal



 + αwater

Vtotal − Vhydration − Vsolute Vtotal (3.17)

where Vsolute , αsolute represent volume fraction and absorption coefficient of solute. Similarly, (Vhydration , αhydration ) corresponds to volume fraction and absorption coefficient of hydration water and αwater corresponds to the absorption coefficient of free water. τ1,hydration  τ1,water since bound water’s response shifts to the MHz-GHz range, while in the THz range, τslow,hydration contribution is practically negligible (see Fig. 3.7). If the ε” of solute is known, the concentration of hydrated water V ) can be found out using the difference in the calculated response (Chydration = hydration Vtotal of solute (Chydration = 0) and obtained dielectric response of solution using THz spectroscopy as discussed in [64, 96]. Since the maximum water absorbance corresponds to frequencies close to 0.01 THz, the sub-THz/THz range is less sensitive to the “slow” relaxation term than the microwave range, as seen in Sect. 3.3. Accordingly, if we consider that εslow term in Eq. (3.11) corresponding to τ1 is the same for free and bound water; then we have the following relationship between εslow of solution and concentration of solute (C solute ) given by ε1 (Csolute ) [19, 49]: εslow (Csolute ) = εslow (0)(1 − Csolute (1 + x))

(3.18)

In this case, x relates to the hydration shell size of the solution and is defined for the specific solution. Figure 3.8 illustrates the ε1 = εslow of the bulk water and glucose solution with an increase in the concentration of glucose [97]. This single Fig. 3.8 The slow relaxation function of Eq. (3.18) as a function of glucose concentration. Reprinted with permission from [97], Copyright © 2015, Elsevier

3.5 Effective Medium Theory

73

parameter approach well describes the experimental spectra of aqueous solutions (particularly sugars and proteins) at frequencies in the sub-THz and THz bands up to 3 THz. The sensitivity to parameters of slow relaxation terms (εslow , τslow ) is highest in the frequency range between 0.05 and 2.5 THz, while the sensitivity to the Lorentz term (εosc ) is low. Therefore, these parameters remain constant for solutions in Table 3.1. It is worth noting that Lorentz terms become significant at frequencies higher than 3 THz [98].

3.5 Effective Medium Theory The inhomogeneity and anisotropy of biological tissues result in strong scattering and absorption of EM radiation. It is a rather difficult task to derive an analytical solution for Maxwell’s equations for electromagnetic radiation in biological sample interactions, as a numerical solution to this type of problem is extremely time-consuming and resource-intensive to solve. However, it is worth noting that in various important cases (human red blood cells, blood glucose levels, bovine serum albumin (BSA), etc.), the tissue structure size is negligibly small compared with the THz wavelength. We can, therefore, consider such a medium to be homogeneous and simplify this problem using an effective medium model. A simple, effective media model can be derived from Maxwell Garnett, Bruggeman, and Landau-Lifshitz-Looyenga’s equations [19, 94, 95].

3.5.1 Maxwell Garnett Model This section will introduce the basic principles for deriving the simplest mixing formula, also known as the Maxwell Garnett (MG) rule [99]. The sample mixture contains spherical inclusions embedded in a background medium. The two components that make up the mixture are known as phases. As for inclusion, it may be described as a guest and the environment as a matrix or a host. If the host component is a homogeneous medium with a permittivity ε2 and spherical particles with a permittivity ε1 represent the guest component. In this case, Maxwell Garnett equation is: ε1 − ε2 εeff − ε2 = f εeff + 2ε2 ε1 + 2ε2

(3.19)

where f = nV is a dimensionless quantity; V is the total volume of the composite medium. Since the mixture rule includes only the volume fraction and the permittivity, it is not necessary for the spheres to have the same size as long as they are all small compared to the wavelength. The model assumes that spherical particles do not cluster and that they are mostly sparse. The effective permittivity (εeff ) is given

74

3 Biological Tissue Interaction with Sub-Terahertz Wave

by: εeff = ε2 + 3 f ε2

ε1 − ε2 ε1 + 2ε2 − f (ε1 − ε2 )

(3.20)

The following equation satisfies the limiting case as f → 0 the εeff = ε2 , i.e., effective permittivity becomes that of the host, and f → 1 the εeff = ε1 effective permittivity becomes that of the inclusion. It is important to note that this formulation is asymmetric in that if the host material and guest material were exchanged, the effective dielectric function εeff would differ [95, 100]. As the difference in the dielectric functions of constituents increases, the asymmetry increases. The assumptions are also violated as inclusion volume increases since the effective density of inclusion changes. Therefore, this model holds true for low solute concentrations.

3.5.2 Bruggeman Model Bruggeman’s model is an improved version of the MG model. In terms of spherical scatterers, the formula has the following basic form: (1 − f )

ε2 − εeff ε1 − εeff + f =0 2εeff + ε1 2εeff + ε2

(3.21)

Bruggeman’s model has the advantage of treating inclusions (ε2 ) and environments (ε1 ) symmetrically. Equation (3.21) acts as a balance between both components of mixing in relation to the unknown effective media, using volume fractions for both the inclusions (f ) and the environment (1−f ). The symmetry property of this equation determines the striking difference between the MG and Bruggeman models. MG approaches are inherently nonsymmetric [101]. This model is under the following restriction for the volume fractions: 2 1 < f < 3 3

(3.22)

The effective permittivity in Bruggeman’s model is given as: εeff =

 1

β + β 2 + 8ε1 ε2 4

β = (3 f 1 − 1)ε1 + (3 f 2 − 1)ε2

(3.23) (3.24)

where f 1 and f 2 are the volume fractions of host and inclusions having permittivities ε1 and ε2 , respectively.

3.5 Effective Medium Theory

75

3.5.3 Landau-Lifshitz-Looyenga Model The Landau-Lifshitz-Looyenga (LLL) model assumes a media that is composed of a guest and a host, modifying the Bruggeman’s model as [102]: 1

1

1

(εeff ) 3 = f 1 (ε1 ) 3 + f 2 (ε2 ) 3 where f 1 + f 2 = 1.

(3.25)

The LLL model allows arbitrary shapes of particles, unlike the MG and Bruggeman models, but it has a very limited scope when it comes to descriptions of optical properties of systems with high dielectric contrast [103]. It is worth noting that biological tissues exhibit various structural features, both microscopic (pores, vessels, etc.) and mesoscopic (cells, membranes, proteins, etc.). Water resides between them all. Inclusions of biological tissue are therefore difficult to measure in terms of their size and shape.

3.5.4 Polder and Van Santen Model for Ellipsoidal Particles The Polder and van Santen (PvS) model for ellipsoidal particles overcomes the strict limitation of Bruggeman’s model, which is valid for particles with a spherical form [104]. According to this model, the effective permittivity of the two-component medium includes that of the host (εhost ) and the elliptical inclusions (εparticle ) leading to the formulation [95]: εeff =

1−

1 3

εhost

 3 f εparticle − εhost i=1

1 εeff +(εparticle −εhost ) Ni

(3.26)

where N i represents the i-th normalized depolarization factor, with x 0 , y0 , z0 as the ellipsoidal axis lengths, N x takes the form: x0 y0 z 0 Nx = 2

∞

0

x02

+ξ

√

dξ (x0 + ξ )(y0 + ξ )(z 0 + ξ )

(3.27)

Same calculations hold for N y and N z . The above models were designed for special cases. Thus, Scheller et al. proposed a quasi-static effective medium theory for heterogeneous dielectric mixtures, which is valid for all mixture concentrations and which also considers particle shape distributions, thus surpassing the limitations of previous approaches [95]. The derivation of effective permittivity for heterogeneous media according to [95] is as follows:

76

3 Biological Tissue Interaction with Sub-Terahertz Wave











εparticle − εeff f =1− εparticle − εhost

 12N −18N 2 −2  (1 + 3N )εparticle + (5 − 3N )εhost 9N 2 −12N −5 × (1 + 3N )εparticle + (5 − 3N )εeff εhost εeff

−3N 2 +3N 3N +1

(3.28)

In the following equation, N x : = N; N y = N z : = (1-N)/2. The model is further extended, taking into account the non-uniformity of the particles for more than one particle in the medium. If there are M different additives with different permittivity levels εparticle,k , each with a corresponding partial volume content f k, jk divided into L k aspect ratios, the resulting volume content of all the additives in the mixture can be described as follows: ⎛ ⎞ ⎛ ⎞ Lk Lk M M     ⎝ ⎝ f = ψk, jk ⎠ = 1 (3.29) f˜k, jk ⎠ where f˜k, jk = ψk, jk f, k=1

j=1

k=1

j=1

Hence the modified f becomes: f =1−

Lk M  

ψk, jk ζk, jk

(3.30)

k=1 j=1

where ζk, jk is given by:  ζk,lk =



εhost εeff



−3N 2j +3N j k k 3N j +1 k





εparticle,k − εeff εparticle,k − εhost



   

1 + 3N jk εparticle,k + 5 − 3N jk εhost  

× 1 + 3N jk εparticle + 5 − 3N jk εeff



12N j −18N 2j −2 k k 9N 2j −12N j −5 k k



(3.31)

Equations (3.28) to (3.31) can be used to model heterogeneous dielectric mixtures for all particle shape distributions and permittivity for arbitrarily shaped particles. The validity of this theory is also confirmed by experimental studies in the THz regime using three different polymeric compounds in [95].

3.6 Dielectric Constant of Biological Materials in Sub-Terahertz Spectrum In the previous sections, we have focused on obtaining the principles of studying the dielectric spectra of biological solutions using THz- Hydration and effective

3.6 Dielectric Constant of Biological Materials …

77

medium theory approaches. The conventional approach to describing the THz dielectric response of biological solutions is described by Smolyanskaya et al. in [19] is as follows. 1.

2. 3. 4.

The known parameters of the water model are systematically analyzed in the subTHz band, and the range of reasonable values for each parameter is evaluated at a particular temperature. These water model parameters are used as the reference parameters for each relaxation process. THz spectroscopy measurement methods are used to examine the dielectric response of biological materials/solutions. Assuming that “slow” Debye relaxation is the only significant process, while other terms introduce negligible contribution to changes in the dielectric permittivity of aqueous solutions in the sub-THz range, the “slow” relaxation parameters (ε1, τ 1 ) are varied to fit the modified Debye/Cole–Cole model in the obtained dielectric response. Finally, the relevant set of parameters is obtained for calculating the dielectric response of biological material/tissue in the sub-THz region.

In this section, we will look into some of the dielectric spectra of biological solutions in the sub-THz/THz region and develop a dielectric model of these solutions based on this approach.

3.6.1 Dielectric Spectra of Saccharide Solutions As mentioned in Sect. 3.4, in the GHz-THz frequency range, saccharide solution absorption spectra exhibit two relaxation processes, named δ- and γ-relaxations [19, 97]. A δ-relaxation occurs when the bound water molecules rotate, whereas a γ-relaxation occurs when the hydrogen bonds that hold the free water molecules reorient. As the resultant spectrum of the solution increases from gigahertz to oneTHz, γ-relaxation dominates. It has been proven that the “fast” relaxation process occurs at frequencies around 0.2–1 THz, as well as the hidden water bending mode observed at 1.5 THz [105]. The main, slow Debye relaxation process is modified when water binds to sugar molecules. A relaxation peak is marked by an increase in relaxation time (τ 1 ) and/or a decrease in amplitude (ε1 ). Several works have tried to examine the complex dielectric constants of varying concentrations of aqueous glucose solutions by studying the variation of these two parameters as opposed to free water in Eq. (3.11) [53, 105–108]. Table 3.2 summarizes the dielectric parameters obtained in the sub-THz band from studies in [49, 97, 106]. In [49, 97], a decrease in amplitude (ε1 ) represents an increase in the concentration of glucose, while the increase in relaxation time (τ 1 ) is studied to be the major factor in increase in concentration [106]. Figure 3.9 shows the dielectric constants of distilled water and different glucose solutions (0.73 M and 1.462 M) at 27 °C [97]. It also demonstrates the dependence of dielectric constants with frequency.

0–0.1

0.15–4

55.8

77.8

51.8

78.3

74.8

1.5

0

1.472

0

1.5 Cole–Cole (α = 0.87)

εs

77.8

Conc. (mol/L)

0

Freq (THz)

0.04–0.15

2.85

5.2

2.39

2.39

ε∞

72

73.1

46

72.02

50

72.02

Δε1

13

8.2

7.93

7.93

τ 1 (ps)

0

0

2.1

2.1

2.1

Δε2

–

–

~235

260

260

τ 2 (fs)

5.2

6.4

~6.5

6.8

6.8

6.9

εreal @ (0.15 THz)

Table 3.2 Parameters of the Debye model for water and glucose concentration of 1.5 M

7.2

9.2

~7.3

9.9

7.3

9.9

εimag @ (0.15 THz)

Temp (°C)

25

27

22

References

[106]

[97]

[49]

78 3 Biological Tissue Interaction with Sub-Terahertz Wave

3.6 Dielectric Constant of Biological Materials …

79

Fig. 3.9 Dielectric constants of distilled water and glucose solutions (0.73 M and 1.462 M) at 27 °C as real and imaginary parts. As indicated in the inset, concentration depends on the frequency of glucose solutions (black circle: 0.2 THz, gray diamond: 1.0 THz). [97], Copyright © 2015, Elsevier

A part of the water in glucose solution transitions from its free state to a bound state and back, which requires knowledge of the spectra of bound water within the THz range to explain the alternation in dielectric parameters. Cherkasova et al. recently developed a method for obtaining dielectric parameters of bound water in glucose hydrated solution in the sub-THz range using wet powder spectroscopy [105]. The dielectric spectra parameters of bound water are the same as that of the free water model [49] except for the lower amplitude ε1 and 30 times higher time of relaxation τ 1 . Hence, the dielectric spectrum of glucose solution in the sub-THz band can be described using the sum of four terms: the free water (εwater ), bound water (εbound ) with concentration Cbound , dissolved crystals (εmolecule ) with concentration Cmolecule , and glucose crystals (εcryst ) with concentration Ccryst , which can be effectively calculated using effective medium theory (LLL used in [105]), which is given by:  1 1 ρ0 ρ0 ρ0 3 3 εeff = Ccryst × + Cbound × + Cmolecule × × εcryst × εbound ρcryst ρbound ρmolecule  3 1 1  

ρ0 3 3 × εmolecule + 1 − Ccryst + Cbound + Cmolecule × × εwater ρwater (3.32) where ρ i represents the density of each of four components and ρ 0 is the density of the solution at this concentration.

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3 Biological Tissue Interaction with Sub-Terahertz Wave

3.6.2 Dielectric Spectra of Blood Blood is made up of red blood cells (erythrocytes), white blood cells (leukocytes), and platelets suspended within plasma. Red blood cells account for 45% of blood volume, plasma for 55%, and white blood cells for just 1%. A large majority of plasma is water (92%) and salts and dissolved proteins (8%). In red blood cells, hemoglobin is found in four different subunits, each of which contains iron and contributes to the blood’s characteristic red color. The white blood cells in the body protect the body from infection. In order to form blood clots (thrombi), platelets are coagulated together with fibrin (a protein) to form a solid mass. All these components affect the tetrahedral structure of water resulting in a change in the dielectric constant of blood with respect to that of water. Several works have used the THz spectroscopy methods to study the whole blood and its components [109–114]. Figure 3.10 shows the sub-THz/THz absorption coefficient and refractive index of the blood and its components [109]. This double Debye model can be used because there are no sharp features in either the blood or water dielectric spectrum. Observation reveals that the absorption coefficient decreases as the amount of water in the blood components decrease. A blood clot, on the other hand, has the same water content as the whole blood. Here, the absorption coefficient has been affected by coagulation and thrombus formation. This way, the thrombus, a protein/fibrin complex, encapsulates the water, preventing it from absorbing THz radiation within the measured range. Table 3.3 shows the parameters of the “slow” relaxation process for blood components of healthy men. The relaxation time of 14.4 ps observed for whole blood is higher than that of free water, suggesting red blood cells delay the relaxation process. There is a difference in relaxation times for water and blood thrombi, suggesting that coagulating blood has a greater effect on the free water present. Red blood cells exhibit very strong τ1 relaxation. The slowed relaxation may well be due to the

Fig. 3.10 Absorption coefficient and Refractive index for whole blood, blood cells, plasma, and a thrombus [109]. Reprinted with permission from IEEE

3.6 Dielectric Constant of Biological Materials …

81

Table 3.3 Parameters of the double Debye model for water and different blood components [109]. Reprinted with permission from IEEE Components

εs

ε∞

ε1

τ 1 (ps)

τ 2 (ps)

Whole blood

130

2.1

3.8

14.4

0.1

Blood plasma

78.8

1.7

3.6

8

0.1

Blood cells

2.5

3.4

23.8

410.8

1.8

Thrombus

130

2.2

3.7

16.1

0.1

Water

78.8

3.3

4.5

8.4

0.1

slowed relaxation of the molecule’s surrounding water, which occurs approximately 102–104 slower than the free water [109, 114]. In diabetes mellitus, the body lacks or produces insulin, resulting in a sugar imbalance. Diabetes is characterized by metabolic imbalances in carbohydrates, proteins, and lipids. This is almost always accompanied by an increased blood glucose level, corticosteroid hormone levels, and other blood levels of some other metabolites. By studying the blood’s dielectric spectrum, THz spectroscopy can help to diagnose diabetes in humans and animals. A new method of studying human blood plasma samples in the sub-THz/THz frequency range using pellets of blood samples has been described by the authors in [113]. The normalized refractive index for diabetes pellets exceeds that of non-diabetic pellets by 9–12%, distinguishing normal from diabetic blood over the entire frequency range. We will discuss the possibility of non-invasive blood sugar detection using the sub-THz band in Chap. 4 using this concept of change in the dielectric spectrum of blood with increased blood glucose levels.

3.6.3 Dielectric Spectra of Protein Solutions Proteins are the functional units of a living organism. The study of molecular processes is closely related to protein dysfunctions, which contribute to the explanation of pathogenic factors or the progression of diseases [115–117]. Drug targets are built up from protein structures which further contributes to the importance of proteins in drug development [118]. As a result, proteins have a very high impact on clinical diagnostics because they often indicate physiological dysfunction when their concentration levels exceed pathophysiological reference ranges. Terahertz spectroscopy is widely used to predict protein conformations and quantitate protein content. THz spectroscopy has been used for investigation of human serum albumin (HSA) and bovine serum albumin (BSA) [119–124], protein conformation [125–128], lyophilized formulations [129, 130], solvated myoglobin [131– 133], tetrameric peptides [134], bioactive peptides [135] and myloid β aggregates [136]. There are many scientific studies addressing problems related to quantitative analysis and parameter effects on hydration and solvation [55, 137–139].

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Serum albumin (a type of globular protein) is the main component of blood and is found in the body’s serum [140]. The ALB gene is responsible for encoding human serum albumin. Several other mammalian forms, such as bovine serum albumin, are chemically similar to human serum albumin. In mammals, serum albumin is the most abundant blood protein and is produced by the liver. The oncotic pressure required for the proper distribution of body fluids between blood vessels and body tissues is maintained by albumin [141]. In addition, it functions as a transport protein for hemin and fatty acids, as well as a hydrophobic steroid hormone carrier. A high or low level of serum albumin may be harmful [142]. If the urine contains albumin, it usually indicates kidney disease. The negatively charged albumins in the body (when ionized in water at pH 7.4) can transport substances with low solubilities. Renal disorders occur when physiologic densities ρ > 20 mg/L (c ≈ 0.3μ M). Albuminuria has been associated with an increase in glomerular filtration of serum albumin [142, 143]. In [144], the authors studied the dielectric permittivity of the aqueous phase of the solution in the presence of BSA using Eq. (3.11). They obtained the dielectric spectrum of aqueous BSA with pH ranging from 2.5 to 10 in the sub-THz/THz region. Relaxation parameters were obtained by fitting Eq. (3.11) for the obtained dielectric spectra. Solution permittivity models include three bands of water spectral data: relaxation bands with relaxation times of about 8.28 and 0.30 ps, and vibrational bands with maximal values of about 180 cm−1 . These parameters deviate significantly at different pH levels. When the values of model parameters of solutions with and without BSA are compared, it appears that the most common effect of BSA is to form hydration shells that are strongly bound to protein molecules. Similarly, in [121], where the dielectric properties of BSA solution were studied within 0.05–3.2 THz, and the response is compared around 0.1, 1, and 3 THz, a low-frequency part is found to be the most sensitive to the amount of BSA in water. A reduction of ε1 was used as a model of solution spectra, that is, the amplitude of a “slow” or γ-relaxation process within the water with increasing concentrations of solute. With this one-parameter variation, the entire frequency range can be described reasonably well in the complex dielectric function spectrum changes. The parameter modeling of protein dielectric response is in line with the approach described at the starting of this section. Figure 3.11 shows the real and imaginary part of permittivities with a change increase in BSA concentration [121]. A significant decrease in the imaginary part is seen with the increase in concentration

(Fig. 3.11b), while minimal change is observed in the real part due to ε∞  Re  1+iε1ωτ1 .

3.6.4 Dielectric Spectra of Biological Tissues Cancer patients are increasingly undergoing tissue-preserving surgery. In this type of surgery, the main challenge is detecting tumor margins. In tissue-conserving surgery, breast-conserving surgery (BCS) relying on lumpectomy, quadrantectomy, or nipplesparing mastectomy is a safe alternative to mastectomy in patients with early-stage

3.6 Dielectric Constant of Biological Materials …

83

Fig. 3.11 a The low-frequency part and b the high-frequency part of the spectra of Im(ε) of BSA solution for several concentrations (inset shows the model curves for a number of values of ε1 ). Reprinted with permission from [121], Copyright © 2018, Springer

breast cancer [145, 146]. During BCS, a layer of healthy tissue around the tumor is resected to ensure the non-existence of tumor cells in the area around the excised part [146]. The presence of a positive margin, denoting the existence of tumor cells on the inked surface, is the key predictor of local recurrence of tumors in the resected area [147]. The most prevalent technique for margin assessment is pathology imaging, in which the excised tumor block is sectioned into 4–5 μm thick slices for microscopic pathology assessment. However, the assessment period ranges from several days to weeks, increasing the cost and duration of re-surgery in case of positive margin detection, which causes additional strain to the patient and the surgeon [148]. The other intraoperative macroscopic assessment techniques have also been investigated for surgical margin assessment, including radiography [149], optical coherence tomography [150], Raman spectroscopy [151], microwave, and Terahertz spectroscopy [152]. The major challenge in radiography is accurate tumor boundaries assessment due to the inability of X-rays to diffuse into the microscopic process [153]. Optical Coherence tomography, Raman, and Terahertz spectroscopy provide high-resolution margin assessment, but due to the shallow penetration depth, these techniques can detect only up to 1–2 mm below the margin, while margin assessment techniques based on microwave frequency range have better penetration but compromises with the spatial resolution due to the use of lower frequency range [154]. The sub-THz band offers better penetration depth than Terahertz and better spatial resolution as compared to the margin assessment techniques working in microwave frequencies [155]. The increase in the concentration of water content in the tumor tissue compared to the normal tissue is responsible for the change in the dielectric spectra of these tissues. Dielectric models have been developed in various works for studying the Debye model parameter changes in the normal and benign tissue [59, 156, 157].

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In [156], authors developed a mixed model for complex permittivity of breast tissue at sub-THz/THz frequencies. Human breasts are composed of inhomogeneous structures of fat cells and proteins. Breast tissue’s dielectric responses are significantly influenced by fatty (adipose) tissue, which has low water content. Accordingly, breast tissue exhibits a flat response in frequencies above 100 GHz, as discussed in [158]. Therefore, while the dielectric properties of the breast tissues are most notable for the peak at around 0.32 THz, fat tissues are also noted to exhibit a similar increase at 0.5 THz. Accordingly, breast tissue exhibits an increase of permittivity in the low-THz range of the THz regime. In addition to this, breast tissue still contains a substantial amount of water, which is the principal absorption source in THz frequencies. As a result, breast tissue’s complex permittivity may also be affected by the dielectric properties of water. Debye relaxation may be more transparent above 1 THz as the frequency of THz increases due to greater water absorption. To describe the Dielectric Spectra of Breast Tissue, the empirical mixture model proposed in [156] uses nonDebye relaxation and Debye relaxation processes. This empirical model takes the formula: ε˜ (ω) = ε∞ +

ωτ1 ε1 + ε2 ε3 σ + + 1 + (J ωτ1 )α 1 + J ωτ2 Jω

(3.33)

In this case, (ωτ1 ε1 + ε2 ) term produces the complex permittivity peak in breast tissue at frequencies below 1 THz. The constants ε1 , ε2 indicate the existence of two dielectric dispersions in the slow relaxation process. The real part of complex permittivity, as well as the imaginary part, have different influences on the values of the parameters introduced in Eq. (3.33), which indicates a potential for cancer discrimination using these parameters. Figure 3.12 illustrates the normalized differences in real and imaginary components of average complex permittivities between tumor tissues and healthy tissues, both fat and fibrous, as shown in [156]. Various tumor margin assessment imaging devices are currently being studied in Fig. 3.12 Normalized percentage difference in the average complex permittivities between the healthy breast tissue groups (fibrous and fat) and breast tumor [156]. Reprinted with permission from IEEE

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the sub-THz domain to capture this permittivity difference property of normal and healthy tissue, which we are going to see in detail in Chap. 5.

3.7 Conclusion The dielectric properties of biological tissues and liquids were studied in this chapter using THz spectroscopy to measure sub-THz properties. It was also discussed how to model the dielectric properties of water, which constitutes the major component in tissue cells, which is present in the interstitial fluid and cell cytoplasm. Dielectric modeling allows for the determination of changes in the properties and concentrations of water molecules present in bio-samples, as well as their state. Different relaxation models of water in the sub-THz and THz bands were discussed, including a modified Debye model and defect migration models. Various composite models have been developed to derive an analytical solution for Maxwell’s equations for electromagnetic radiation in biological samples. These models were discussed in-depth with examples available in the literature for sub-THz and THz frequencies. Among them are the effective media theory models of Bruggeman’s effective permittivity and the LLL model. With these models, we have analyzed the dielectric spectra of biological materials such as blood, protein, and tissues.

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148. El-Shenawee M, Vohra N, Bowman T, Bailey K (2019) Cancer detection in excised breast tumors using Terahertz imaging and spectroscopy. Biomed Spectrosc Imaging 8(1–2):1–9 149. Oakley KL, Going JJ (1995) Specimen slice radiography of cancer in breast conserving excisions. J Clin Pathol 48(11):1028–1030 150. Ge X, Chen S, Chen S, Liu L (2021) High resolution optical coherence tomography. J Light Technol 39(12):3824–3835 151. Haka AS, Volynskaya Z, Gardecki JA, Nazemi J, Lyons J, Hicks D, Fitzmaurice M, Dasari RR, Crowe JP, Feld MS (2006) In vivo margin assessment during partial mastectomy breast surgery using Raman spectroscopy. Cancer Res 70(20):7882–7893 152. Dixon JM, Renshaw L, Young O, Kulkarni D, Saleem T, Sarfaty M, Sreenivasan R, Kusnick C, Thomas J, Williams LJ (2016) Intra-operative assessment of excised breast tumour margins using Clear Edge imaging device. Eur J Surg Oncol 508(1–3):31–45 153. Santaolalla A, Portieri A, Fitzgerald AJ, Purushotham A, Owen J, Grootendorst MR, Cariati M, Young MR, Pepper M, Van HM, Pinder SE, de Koning SGB, Wallace VP (2017) Use of a handheld Terahertz pulsed imaging device to differentiate benign and malignant breast tissue. Biomed Opt Express 8:2932–2945 154. Arezoo M, Sally G, Asal R, Kamran K (2017) Review of breast screening: toward clinical realization of microwave imaging. Med Phys 44:e446–e458 155. Kaurav P, Koul S, Basu A (2021) Electromagnetic characterization of breast tissue phantoms in D band regime. IEEE J Electromagn RF Microw Med Biol. https://doi.org/10.1109/jerm. 2021.3078645 156. Truong BCQ, Tuan HD, Fitzgerald AJ, Wallace VP, Nguyen HT (2015) A dielectric model of human breast tissue in Terahertz regime. IEEE Trans Biomed Eng 62(2):699–707 157. Shiraga K, Ogawa Y, Suzuki T, Kondo N, Irisawa A, Imamura M (2014) Characterization of dielectric responses of human cancer cells in the Terahertz region. J Infrared, Millimeter, Terahertz Waves 35(5):493–502 158. Purushotham AD, Provenzano E, Pickwell-MacPherson E, Pepper M, Ashworth PC, Pinder SE, Wallace VP (2009) Terahertz pulsed spectroscopy of freshly excised human breast cancer. Opt Express 17:12444–12454

Chapter 4

Non-invasive Sub-Terahertz Blood Glucose Measurement

Abstract Diabetes monitoring methods available today for the ever-increasing number of diabetic patients worldwide are invasive, painful, time-consuming, and a constant burden on the household budget. For these reasons, non-invasive glucose monitoring is heavily researched and is highly desired by many companies. The first part of this chapter will discuss various guidelines and parameters that are important to consider when developing blood glucose measurement devices. As we move on, we will review some of the non-invasive glucose measurement methods that use the non-ionizing electromagnetic spectrum. The techniques are categorized according to the parametric estimation of blood/tissue properties and intrinsic glucose properties. In Sect. 4.3, we will study the complex permittivity variation with glucose concentration using the double Debye model to analyze the dielectric properties of aqueous glucose solutions in the sub-THz region. The purpose of Sect. 4.4 is to discuss the importance and use of tissue-mimicking phantom modeling as a method of validating these non-invasive monitoring devices for blood glucose levels. As a part of the last section, we will explore the recently developed reflection and transmission derived sub-THz/THz sensors suitable for this application.

4.1 Introduction Glucose provides energy to the body, which helps it to function at its best. If our glucose levels are normal, it commonly goes unobserved. Any deviation from the suggested limits of glucose levels will impact the ordinary functioning of the body [1]. Diabetes mellitus is the result of inadequate insulin production or an improper response of cells to insulin [2]. Blood sugar is regulated by insulin. Hyperglycemia, or elevated blood sugar, is a common complication of uncontrolled diabetes that damages the nerves and blood vessels over time and many other body organs. By 2045, the World Health Organization (WHO) estimates 693 million diabetic patients worldwide, up from 463 million in 2019 [3]. By 2030, diabetes is projected to increase its total annual cost (medical and non-medical) by 53%, from $407.6 billion in 2015

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. K. Koul and P. Kaurav, Sub-Terahertz Sensing Technology for Biomedical Applications, Biological and Medical Physics, Biomedical Engineering, https://doi.org/10.1007/978-981-19-3140-6_4

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to more than $622.3 billion. The chronic complications of diabetes include heart disease, kidney failure, strokes, vision loss, and damage to the nervous system [4]. Type-I diabetes, Type-II diabetes, and gestational diabetes are the three types of diabetes. There are also other types of diabetes caused by other factors, including monogenic diabetes syndromes, diseases of the exocrine pancreas, and chemically induced diabetes (which occurs because of glucocorticoids, HIV/AIDS treatment, or transplantation) [5]. Diabetes type 1 usually happens because of a lack of insulin produced by beta cells in children [6–8]. Approximately 5–10% of diabetic patients have this form. Most diabetic patients suffer from the second type, representing over 90%, which is related to an unhealthy lifestyle and inherited characteristics [9]. This condition is marked by insulin resistance and can be accompanied by decreased insulin secretion [10, 11]. Pregnant women usually develop the last category, which either vanishes or becomes Type-II diabetes after delivery [12–14]. In Hyperglycemia, the glucose level is above 200 mg/dL, whereas in Hypoglycaemia, it is below 70 mg/dL [15, 16]. There are no permanent treatments for either case, which requires daily monitoring of blood glucose level with active treatment (insulin injections, bariatric surgery) to improve diabetic patients’ quality of life. A glucose determination method based on glucose oxidase (GOx) has been widely used since Clark and Lyons proposed it in 1962 [17]. In essence, most glucose meters rely on electro-enzymatic reactions, a procedure requiring a finger prick to obtain a drop of blood (−1 μL) and apply it directly to a disposable testing strip [18, 19]. Despite the accuracy of this type of invasive device, it still has the potential for causing physical pain and infection with frequent pricking. It is estimated that testing strips cost $750 per patient annually [20]. To replace finger-prick devices and achieve continuous blood glucose monitoring (CBGM), semi-invasive or minimally invasive devices have been developed. An implantable subcutaneous sensor measures glucose concentrations in interstitial fluid (ISF). Despite this, the sensitivity continually declines as a layer of protein builds upon the surface of the sensor, which necessitates frequent calibration [19, 21]. In recent years, many efforts have been made to develop truly non-invasive glucose sensors. EM wave sensing has garnered significant attention due to its rich interactions, such as absorption, scattering, and transmission, with various compounds inside the body. As mentioned in Chap. 1, the EM spectrum is classified into ionizing and non-ionizing waves. Non-ionizing radiation is reasonably safe and well suited for non-invasive biological sensing and imaging applications. The measurement of glucose levels in the non-ionization radiation domain consists of two main categories: (1) measurements based on intrinsic glucose properties, (2) measurements based on tissues’ dielectric properties [19]. As a glucose molecule, glucose has three intrinsic properties: absorption coefficient, specific optical rotation, and Raman shift. The first category involves the use of infrared waves, which use these properties to extract glucose concentration. The second category requires the use of RF, microwave, mmW, sub-THz, and THz waves using light scattering coefficient, tissue permittivity, and tissue conductivity [19]. Figure 4.1 illustrates the two categories for non-invasive glucose measurement based on measuring intrinsic properties of the glucose and properties of tissues.

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Fig. 4.1 Non-ionizing and non-invasive glucose level measurements using EM waves

This Chap. begins with a review of non-ionizing sensors for non-invasive glucose measurement and continues with recent developments in using sub-THz waves for this process in the following sections. Throughout this article, we will provide stepby-step information about the development of sub-THz glucose sensors. We will begin with a discussion of the penetration depth of sub-THz waves in blood tissue in Sect. 4.3, followed by a discussion of phantoms for mimicking blood properties in Sect. 4.4. The final section discusses the recently developed non-invasive glucose measurement devices utilizing the reflection and transmission properties of the subTHz waves.

4.2 Non-ionizing Blood Glucose Measurement Techniques Using EM Waves Several reviews have been published regarding non-invasive glucose measurement. Bachache et al., Todd et al., Joshi et al., Kumar et al., and Gonzales et al. recently provided extensive overviews associated with non-invasive glucose measurement techniques [22–26] from electric and optical methods to nanotechnology. In the nonionizing EM field, many works of literature have focused on particular frequency ranges like optical methods [27–29], THz spectroscopy [30, 31], microwave sensing [32, 33], and NIR spectroscopy [28, 29, 34–37]. Before proceeding to the review of different types of non-invasive glucose measurement methods based on intrinsic properties of glucose and dielectric properties of tissues, we need first to understand the penetration depth of the EM waves with respect to the frequency, which will be discussed in the following subsection followed by ISO requirements and performance evaluation parameters for evaluating the effectiveness of the non-invasive glucose sensor.

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4.2.1 Penetration Depth of the EM Wave with Respect to Frequency EM waves penetrate a material at different levels, depending on its penetration depth. In its fundamental sense, it is the depth at which the power or the intensity of radiation inside the material drops to 1/e (about 37%) of its original value at the surface [38]. EM radiation incident on the surface of a material is partially reflected back and partly transmitted into it. An EM field transmitted into the material interacts with the atoms and electrons within it. EM fields can travel very far into a material or can disappear very quickly, depending on the nature of the material. Penetration depth for a given material is generally determined by the frequency. Penetration depth (δ p ) is related to attenuation coefficient (α) (described in Appendix A.2) by: δp =

1 α

(4.1)

The penetration depth of different biological materials, including water and ethanol, has been plotted in sub-THz to THz frequency ranges [39]. It is demonstrated that penetration depth and refraction index decrease with the frequency while attenuation coefficient increases with the frequency which is also illustrated in Fig. 4.2 for water and skin.

4.2.2 Performance Evaluation Parameters Performance evaluation parameters are used to test the accuracy and effectiveness of glucose monitoring devices. Metric measurements have been developed to evaluate the accuracy, including relative measurements with references and error grids [21, 26]. A relative measurement refers to comparing non-invasive techniques and devices with invasive blood glucose meters measured simultaneously. From a statistical and clinical perspective, several indicators are used to quantify performance [40]. It is possible to estimate the level of correlation between two data sets using the coefficient of correlation R within ±1; its value always varies between positive and negative values, with a positive value indicating the same variation trend and a negative value indicating the opposite trend. A second indicator is the R-squared (R2 ) value, which measures the quality of linear regression. The root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are other variables used to compare predicted values with reference values [41]. However, the values of these parameters are heavily dependent on the details and characteristics of a study. Because of this, comparing it using correlation techniques may result in misinterpretations, as suggested by Bland and Altman [45]. Hence, Bland et al. provided a new statistical approach to assess the degree of agreement.

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Fig. 4.2 Real index of refraction (n), specular reflection loses (Rs ), absorption coefficient (μa ). and optical penetration depth (δ) of water obtained from Wilmink et al. [42], blue line; Jepsen et al. [43], red line; Nazarov et al. [44] green line and Skin spectra obtained from Wilmink et al.; [42], black line. Reprinted with permission of [39]. Copyright © 2011, Gerald J. Wilmink et al.

Figure 4.3 shows the Bland–Altman plot used for this purpose. In this figure, the solid black line represents the mean difference between measurements and references (d); Fig. 4.3 Bland–Altman plot for evaluating accuracy of glucose sensor. The black line represents mean difference between reference and predicted value. The dashed line represents 1.96 × standard deviation

Fig. 4.4 Clarke’s error grid analysis for test dataset of different concentrations [47]. Reprinted with permission from IEEE

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Predicted Concentration [mg/dl]

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460

E

C

B

345 B 230 D

D

115

0

A 0

C

E

115 230 345 460 Reference Concentration [mg/dl]

the two dotted lines represent the limits of agreement (d ± 1.96SD; SD is the standard deviation between the measurements). As well as the above statistical methods of evaluating the accuracy, Clarke suggested using a scatterplot for describing the clinical accuracy of glucose meters, which has become the “gold standard” [46]. Figure 4.4 shows the Clarke Error Grid (CEG), divided into five parts, according to their respective deviations from the reference, where A contains values within 20% of the reference. B contains predictions that are beyond 20% error but do not require intervention. These two regions have clinically acceptable data. Predictions falling into region C will result in overcorrection of normal glucose levels, while predictions falling into region D will fail to detect abnormal glucose levels for prompt treatment. In regions E and F, treatment will be erroneous and dangerous. Obtaining measurements in these three regions (C, D, and E) is not helpful in patients’ daily care. To determine the minimum sample quantity that can be measured, measurements are limited by the limit of detection (LOD). In other words, it measures how sensitive and noisy the system is.

4.2.3 ISO 15197: Accuracy Assessment Standard In addition to medical devices, food-borne illnesses, environmental sustainability, environmental management, and information technology, the International Standards Organization (ISO) also develops specifications for procedures, services, and production for these products in a wide range of industries [48]. With 162 national standards bodies, ISO is a global player, influencing regulations at many government agencies worldwide. The new ISO 15197:2013 standard was released in 2013 to provide worldwide glucose monitoring devices and systems standards [49]. The new standard is more stringent than its predecessor, ISO 15197:2003, and new devices are required to meet

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tighter accuracy requirements. Nevertheless, patients and clinicians will benefit from adhering to the new guidelines as they will feel more confident that glucose readings are accurate and reliable on a daily basis. Based on the new standard, 95% of blood glucose results must be within ± 15 mg/dL at glucose concentrations less than 100 mg/dL or ± 15% at glucose concentrations of 100 mg/dL or more, compared to a reference laboratory method. The European Union began using EN ISO 15197:2015, a harmonized version of ISO 15197 released in 2015. But there was no change to the requirements for glucose meter performance evaluation in this version [50, 51].

4.2.4 Non-invasive Glucose Measurements Using Intrinsic Properties of Glucose Intrinsic glucose property sensing techniques involve high-frequency infrared/optical waves. The EM wave is treated as a light source that interacts with atoms within the tissue. This interaction results in three phenomena, i.e., absorption, transmission, and scattering of light. These phenomena are based on three factors: (1) frequency of incident light, (2) structural properties of tissue, (3) optical parameters like attenuation coefficient, refractive index, and scattering coefficient [52]. These phenomena result in two types of light scattering: elastic and inelastic scattering [53]. The energy of scattered light in elastic scattering is equal to incident light (i.e., λ1 = λscattered light ), but in inelastic scattering, the energy is less or greater than incident light (i.e., λ1 = λscattered light ) [19]. The elastic scattering includes Rayleigh scattering as well as Mie scattering. During Rayleigh scattering, the size of the particles involved in the scatterings is smaller than the wavelength of the incident light (λ1 ). On the other hand, when Mie scattering takes place, the size of the particles is the same as the wavelength of the incident light [54]. As compared to Mie scattering, Rayleigh scattering depends more strongly on wavelength, and the intensity of scattered light in Rayleigh scattering is proportional to (1/λ1 4 ). Raman scattering and fluorescence are examples of inelastic scattering. The inelastic scattering is negligible in comparison to elastic scattering in optical frequency [55]. Non-invasive glucose measurement methods employ near-infrared/mid-infrared (NIR/MIR) absorption, optical polarimetry, and Raman spectroscopy to determine intrinsic glucose properties [19].

4.2.4.1

Near-Infrared and Mid Infrared Spectroscopy

Near-Infrared (NIR) and Mid Infrared (MIR) spectroscopy measurement techniques use 700–2500 nm and 2500–25,000 nm wavelength ranges, respectively, to acquire spectroscopic information of tissue samples [56, 57]. These techniques measure

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glucose concentration based on the light absorption phenomenon given by BeerLambart law. This model assumes that the scattering of the light is negligible compared to the absorption of the light. Although in a practical scenario, scattering due to multi-tissue components observes in deviation of this model resulting in measurement errors [28]. Another major issue with non-invasive glucose measurement using these techniques is penetration depth. The penetration depth of the NIR sensing system is a few hundred microns while it’s in a few microns for MIR-based glucose sensors [19]. The average thickness of skin tissue, including stratum corneum, epidermis, and dermis, is 1.5 mm, making it difficult for light to penetrate to the subcutaneous tissue that contains the blood vessels. This is another major challenge that needs to be overcome by NIR/MIR-based blood glucose measurement sensors.

4.2.4.2

Optical Polarimetry

Polarimetry is another technique that uses the “chirality” property of glucose. The chiral glucose molecules can rotate the plane of polarized light by an angle α’, in a clockwise direction [28]. A rotational amount is proportional to the concentration of the analyte and laser’s temperature and wavelength, which is usually somewhere between the upper and lower regions of the NIR (~780–400 nm). The lack of optical rotation associated with physiological glucose levels, the presence of other active molecules, and the high absorption of light in the skin and tissues make it virtually impossible to use optical polarimetry on the skin [58]. It is possible to use it in the eye’s anterior chamber due to its excellent optical properties. A light source emits light that is polarized before reaching the eye [59, 60]. In the next step, the light is analyzed to determine its rotational angle and intensity. Temperature sensitivity and motion sensitivity are some of the major challenges of this technique—a small displacement of the subject results in erroneous results [61].

4.2.4.3

Raman Spectroscopy

The third type of non-invasive glucose sensor based on intrinsic properties of glucose includes Raman spectroscopy. A high-intensity light source in the NIR range is used in Raman spectroscopy, along with a very sensitive Raman spectrum detector [62]. A portion of light with a monochromatic frequency is scattered when it interacts with matter. The scattering is mostly elastic (Rayleigh scattering and Mie scattering), with a very small percentage of inelastic. When inelastic scattering occurs, light with multiple frequencies and wavelengths is generated. Elastic scattering has the same frequency and wavelength as incident light [28]. The majority of scattered light is elastic, and about one in every million scattered photons undergoes inelastic scattering. Raman shift is a wavelength difference between the initial and final vibrational states of a molecule. The Raman spectrometer depends on rotations and vibrations within molecules. The peak height of the Raman spectrum depends on the wavelength

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of the light source and the concentration of the substance [63]. Light scattered by molecules or chemical structures changes its frequency depending on the molecules themselves and does not depend on the light source’s wavelength. Several physiological factors affect Raman-based measurements of glucose. The Raman fingerprint of glucose can be affected by differences in the characteristics of skin tissue in different individuals [19]. Raman spectra are also affected by proteins in the blood, which are influenced by light sources [64]. Raman scattering signals are weak when compared with the peaks associated with elastic scattering. It is also challenging to analyze blood glucose by Raman spectroscopy due to the low glucose concentration in blood compared to other biological components. These challenges need to be overcome to increase the performance of this method for blood glucose concentration measurement application. Table 4.1 summarizes the principle, advantages, and disadvantages of these techniques. Table 4.1 Non-invasive glucose measurement EM wave techniques based on intrinsic properties of glucose Technique

Principle

Advantage

Disadvantage

NIR

Absorption and scattering of light in the 700–2500 nm range

• Glucose detection is possible in the presence of other interfacing substances such as plastic or glass • Water transparent in the NIR band

• High scattering level • Concentrations are too low for accurate detection • Selectivity issue in multi-tissue components

MIR

Absorption and scattering of light in the 2500–25,000 nm range

• Low scattering • Penetration depth is just a few • The absorption bands micrometers are more specific and • Strong water stronger than NIR absorption • Expensive

Polarimetry

Rotation of the • High resolution polarization plane of • Easy Miniaturization a light beam of optical devices (Chirality)

• Sensitive to temperature changes and motion • Sensitive to the displacement of the subject • Acquisition time − 30 min

Raman spectroscopy

Raman effect

• Selectivity issue in multi-tissue components • Weak inelastic scattering components • Large acquisition time

• Less sensitive to temperature changes • Less sensitive to water • High specificity

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4.2.5 Non-invasive Glucose Measurements Using Dielectric Properties of Tissue Techniques based on dielectric properties of tissue are classified into two categories: (1) optical properties-based glucose measurement techniques that involve tissue scattering coefficient and blood refractive index, (2) electrical properties of tissue including permittivity and conductivity sensitivity to glucose concentration. The first category usually involves high frequency waves from THz to the optical region. Scattering and occlusion spectroscopy, optical coherence tomography, and THz spectroscopy fall in this category. The low-frequency waves from radio waves to the sub-THz waves sense the glucose levels using permittivity and conductivity properties of tissues falling in the second category.

4.2.5.1

Occlusion Spectroscopy

The occlusion spectroscopy-based method relies on the light scattering principle, which is inversely proportional to glucose concentration [65]. During this procedure, the tissue is deliberately pressed. Above the systolic pressure, an increase in blood pressure can obstruct blood flow, causing red blood cells to clump together and scatter particles to get larger in size. Dynamic changes in the blood flow are observed, which increases the scattered signal. Furthermore, the momentary cessation in blood flow results in a higher SNR value for the detected signal. Hence, the sensitivity for measuring glucose is improved along with robustness for accurate measurements. The performance of this technique is largely affected by the presence of protein in blood plasma which plays a dominant contribution in blood refractive index [66]. The variation in collagen levels in the skin with age and blood osmolality also interfere with the accuracy of glucose-sensing due to the variation in scattering parameters as studied in [67, 68].

4.2.5.2

Optical Coherence Tomography (OCT)

Optical Coherence Tomography (OCT) imaging technology relies on low coherence interferometry and coherent radiation to detect micrometer-level changes in the optical properties of bio tissues [69]. Despite being initially developed for the eye’s tomographic imaging; it can measure glucose concentration through the skin with acceptable accuracy and specificity. This technology involves radiating a wavelength of coherent light between 800 and 1300 nm onto the skin [70, 71]. An interferometer senses a backscattered signal from a photodetector using the backscattered radiation combined with a reference. If glucose concentration increases, it will increase the refractive index and decrease the scattering coefficient, resulting in a mismatch reduction in the refractive index between the medium and the reference, proportional to its concentrations.

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The major challenges faced by OCT glucose measurement techniques include some physiological and experimental conditions. The major challenge is the scattering of light by other components of blood, including RBCs, protein, and water [19]. Motion artifacts and temperature fluctuations in tissue also interfere with the accuracy of the system. Variation of skin thickness with change in temperature leads to prediction error of 0.3 ± 0.09 mmol/l for 1 ºC change as studied in [72].

4.2.5.3

THz Pulsed Imaging and Time-Domain Spectroscopy

We discussed in Chap. 1 that TPI measures radiation absorption by using singlefrequency laser pulses using the time required for photons to cross a sample [26]. It uses the same absorption and scattering principles as other spectroscopic techniques. Light traveling through the sample will follow different paths toward the detector because of multiple internal reflections, while other photons will be dispersed as a result of total scattering. It is possible to detect the optical properties of a medium, including glucose concentration, by analyzing the time-of-flight distribution of detected photons, the changes in pulse shape (pulse broadening caused by scattering), and the absorption level of the medium. As with TPI, THz-TDS measures the travel time (phase information) of reflected and scattered signals and absorption by the medium by using CW pulses in the time domain. Using a single scan, we can measure the refractive index and the spectrum of complex permittivity. Additionally, certain advanced techniques can be used to extract frequency-dependent information, for example, dynamic range, bandwidth, and signal-to-noise ratio [73, 74]. Both TPI and THz-TDS have the advantage of being immune to background noise. A few issues, including issues related to measurement time and complex measurement setup, require further investigation [26]. Reflection and transmission are the two key modes of operation in THz-TDS, as in many other technologies. As a result of the high-water absorption levels in the THz band, the transmission mode does not produce satisfactory results. In the last few years, research has been directed more on the reflection mode [74]. Because high-frequency THz radiation can only reach a limited number of locations beneath the skin that contain large amounts of blood for examination, low depth penetration continues to be a significant barrier. The other difficulties entail physiological changes that influence the skin, such as changes in the osmolarity of the blood, fluid loss in the cells, and aggregation of erythrocytes, among others.

4.2.5.4

Bioimpedance Spectroscopy

In bioimpedance spectroscopy, a small AC signal below 1 MHz is used to measure impedance levels within the tissue [75]. The electrical circuit of biological tissue can be modeled as a series of resistors and capacitors. Capacitance and resistance are determined by the cell membrane structure and the body fluid (intracellular and

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extracellular), respectively. This means that the impedance of tissue depends on the electric properties of the cell membrane. In tissue, conductivity refers to the movement of ions within the biological fluid, and permittivity refers to the tissue’s ability to store or rotate molecular dipoles in the presence of an electric field. Almost half of the blood volume is taken up by RBCs, so their membrane contributes significantly to the total tissue impedance and affects the equivalent circuit capacitance in the tissue [76]. Dielectric impedance spectroscopy evaluates blood glucose-induced changes in the conductivity and permittivity of red blood cells (RBCs) membrane. It is proposed that variations in plasma glucose concentration induce changes in sodium (Na+) and potassium (K+) concentrations within RBCs’ membranes, suggesting a direct correlation between the two variables. So bioimpedance spectroscopy measures resistance and conductivity by applying alternating currents with a known intensity [77]. The major challenge of this technique is the variation of RBCs morphology between human subjects [78]. Other factors affecting the accuracy of the method include motion artifacts, sweat, and temperature variations that can change the level of impedance, leading to erroneous results [75].

4.2.5.5

Microwave/Millimeter-Wave Sensing

A lower energy per photon and less scattering in the tissue are the features of MW and mmW radiation, demonstrating that they can penetrate deeper into the tissues to reach regions with sufficient blood levels, thus providing more accurate glucose readings [32]. Using such characteristics, mmW and MW techniques are used to measure the reflectance, transmission, and absorption characteristics of tissues and blood in those bands to correlate their permittivity and conductivity with the glucose concentration in the body. Reflection-based sensing techniques use the reflection parameter S 11 to identify amplitude and phase variations in the reflected signal that occur when glucose levels vary in the blood [79]. An antenna, an open-ended coaxial line, or a waveguide are all possible sensors for measurements on a wide frequency band using a VNA [80–83]. As the wave penetrates the skin layers, it reaches depths where there is sufficient blood to detect changes in permittivity, which are interpreted as changes in impedance or admittance, thus varying S11 proportionally. The transmission methods use both S 21 and S 11 parameters to calculate the attenuation and phase insertion of transmitted signals as glucose levels are varied [84–88]. They use similar hardware to reflection methods in a duplicated configuration to work as transmitter and receiver of the wave. Resonant perturbation is an extension of reflection and transmission in the sense that it uses a near-field sensor with a high Q-factor [89–94]. The objective is to measure changes in resonant frequency, quality factor, and 3dB bandwidth and correlate those with variations in the dielectric properties of the test medium. Consequently, the sensors used in resonant perturbation operate in a very narrow frequency

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range. Microstrip patch antennas and waveguides, split-ring resonators, and dielectric resonator antennas (DRAs) are examples of such sensors [26]. Temperature variations, motion artifacts, sweat, and variation in tissue hydration state can impact the accuracy of MW and mmW wave glucose sensors. Table 4.2 summarizes the principle, advantages, and disadvantages of the glucose-sensing techniques based on tissues’ dielectric properties. From the current review of the existing non-invasive glucose measurement sensors utilizing either the lower frequency domain of the EM spectrum, including the MW (3–30 GHz), mmW band region (30–100 GHz), or the higher-frequency range (THz, infrared and optical region; >300 GHz) it has been observed that: (1)

(2)

Methods and devices utilizing the lower frequency bands, i.e., MW and mmW frequency bands, provide a good penetration through human tissues and are used for non–invasive examination of glucose levels [89, 95]. However, these systems compromise with the sensitivity due to larger wavelengths. For instance, in the recent studies in the MW domain, frequency resonance-based sensors in [96] and [97] can detect a change of 40 and 32.4 mg/dl, respectively, and the setup in [98] is shown to detect 20 mg/dl change in glucose concentration accurately. Similarly, a millimeter-wave measurement system working in a 60–80 GHz band achieved 50 mg/dl resolution [86]. On the other hand, the glucose sensors using a higher-frequency spectrum (THz, infrared and optical region) are highly sensitive to changes in glucose levels, as demonstrated in [74] in the THz frequency band. Similarly, in [99], a highly susceptible glucose sensor is developed based on Localized Surface Plasmon Resonance (LSRR) in optical frequency. However, as the frequency increases, the penetration of waves inside the tissue decreases, and frequencies above 300 GHz can penetrate only up to a few microns [100, 101], limiting the use of these higher-frequency glucose sensors for non-invasive glucose level examination.

The sub-THz spectrum lying between the mmW and THz band provides a good balance between the penetration depth and sensitivity [47, 102]. In the following sections, we will look at some of the features of sub-THz waves regarding glucose level detection, thereby discussing the possibility of using sub-THz waves for developing non-glucose sensors.

4.3 Sub-Terahertz Spectrum for Non-invasive Evaluation of Glucose Levels The first step toward developing a non-invasive glucose sensor using EM waves involves assessing the penetration depth of the wave inside the biological tissue. For the transmission and reflection-based measurement setup, the wave needs to penetrate inside the skin to reach the blood vessel in the subcutaneous layer of the skin. In this section, we will discuss the penetration depth of sub-THz waves inside the

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Table 4.2 Non-invasive glucose measurement EM wave techniques based on dielectric properties of tissue Technique

Principle

Advantage

Disadvantage

Occlusion Spectroscopy

Light scattering is inversely proportional to glucose concentration

• High SNR • Errors due to variation in skin • High sensitivity scattering coefficient • Selectivity issue in multi-tissue components.

OCT

Low coherence interferometry and coherent radiation to detect micrometer-level changes in the optical properties of bio tissues

• High SNR • High resolution • Not vulnerable to cardiac pressures.

THz TPI/TDS

THz pulse absorption and scattering

• Better • Large acquisition penetration than time • Low spatial infrared and resolution optical-based techniques • Less scattering compared to infrared and optical-based techniques

Bioimpedance spectroscopy

Measurement of impedance levels within the tissue using a small AC signal below 1MHz

• Inexpensive • Easy measurement technique

Microwave and millimeter-wave sensing

Reflection/transmission/resonance phenomena of MW/mmW on tissue

• Better • Sensitive to penetration than temperature infrared and changes and optical-based motion. • Sensitive to techniques water content • Less scattering (sweat) compared to infrared and • Poor selectivity optical-based techniques.

• Sensitive to temperature changes and motion. • Sensitive to heterogeneous tissue environment.

• Sensitive to temperature changes and motion. • Sensitive to the RBCs morphology • Sensitive to water content (sweat)

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blood tissue followed by the change in the dielectric response of glucose levels with change in concentration in this band which plays an important role in determining the efficiency of the system.

4.3.1 Penetration Depth of Sub-Terahertz Wave Inside Blood Tissue The skin of the human body is a complex, multilayered structure in which multiple components are intertwined, including blood arteries and capillaries, the oil glands, various branches of the nervous system, hair, and their follicles. Figure 4.5 depicts human skin tissue layers which include the stratum corneum layer whose thickness varies in 10–20 μm, the epidermis (30–100 μm in thickness), the dermis (900– 1500 μm in thickness), and the subcutaneous tissue (1000–5000 μm in thickness) [19]. In the epidermal layer, interstitial fluid accounts for around 15–35% of the total volume, and there are no blood vessels. The dermis layer comprises arterioles, venules, capillaries, and interstitial fluid, which accounts for around 40% of the total volume. In addition to fat storage, the subcutaneous tissue contains some interstitial fluid (albeit less than in the dermis layer) and blood arteries connecting the dermis to the blood circulating throughout the body [103, 104]. The penetration depth of skin has been studied in various pieces of literature for sub-THz and THz waves using THz spectroscopy [105–108]. Figure 4.6 shows the refractive index and absorption coefficient of skin from the arm using THz pulsed spectroscopy [105]. The penetration depth of sub-THz wave skin varies between 0.1–0.3 mm, as also shown in Fig. 4.2 [42]. The penetration depth decreases to 30 microns in THz, providing the spectral information until epidermis above THz. The

Fig. 4.5 Different layers of skin tissue with their respective thicknesses

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Fig. 4.6 Refractive index and absorption coefficient of arm skin obtained using THz spectroscopy. Highlighted band represents the sub-THz region [105]

sub-THz wave is able to penetrate below the epidermis and can provide spectral information of the dermis as well [74]. Similarly, Fig. 3.10 (Chap. 3: Sect. 3.6) shows human blood’s sub-THz absorption coefficient and refractive index and its different components like plasma cells, RBC, and thrombus. The figure shows that the absorption coefficient of blood is around 100 cm-1 in the sub-THz band, leading to a penetration depth of 0.1 mm inside blood tissue by the sub-THz wave. The absorption coefficient increases with the frequency, as visible in Fig. 4.6 and Fig. 3.10, leading to a decrease in penetration depth inside the tissue. The penetration depth is the indicator of attenuation of EM waves inside the tissue. The more the penetration depth, the better the Signal-to-Noise Ratio (SNR) of the signal [109]. Hence, sub-THz-based measurement setups tend to have better SNR than high THz spectrum measurement setups.

4.3.2 Dielectric Properties of Glucose with Variable Concentration in Sub-Terahertz Spectrum The next important consideration while developing a non-invasive EM-based glucose measurement device involves the behavior tracking of EM waves with changes in aqueous glucose concentration. This behavior is studied by analyzing the change in dielectric properties of glucose with a change in concentration in the sub-THz band. As seen in Chap. 3 (Sect. 3.6.1), the change in the dielectric properties of different glucose solutions is due to EM waves’ dispersion propagating inside the medium.

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This dispersion property has been mathematically modeled in [110–112], demonstrating the dependence of the frequency of interest on these solutions. In the subTHz domain, a two-pole Debye model can be used for estimating the permittivity of different samples of glucose, making use of the following [74, 113]: ε(ω) = ε∞ +

ε2 ε1 + 1 + j ωτ1 1 + j ωτ2

(4.2)

where ε(ω) is the complex permittivity of glucose solution expressed as a function of frequency ω, ε∞ is the high-frequency dielectric constraint, ε1 and ε2 are the contributions to the permittivity of 1st and 2nd order Debye terms, τ1 represents the relaxation time for “slow” relaxation and τ2 corresponds to the fast Debye terms. As studied in Chap. 3, an increase in the relaxation time (τ1 ) of bound water is observed when water molecules are physically or chemically attached to biomolecules (sugars, proteins), which influences the form of solutions’ GHz and sub-THz spectra [74]. Cherkasova et al. in [110] studied the change in τ1 with an increase in the concentration of glucose, where it was observed that when the concentration of glucose in water increased from 0 to 4 mol/L, τ1 increased from 10 to 100 ps which was in line with what was observed by Fush and Kaatze in [114]. A similar spectral change was observed by real blood samples in which  a 24 mM increase in glucose concentration in the blood led to a decrease of ε1 τ1 by 1.2 times [115]. Figure 4.7a, b shows the real and imaginary parts of permittivity of glucose levels lying in the range 70–145 mg/dl with increments of 15 mg/dl considering the usual human blood glucose concentration level ranging from 70 to 140 mg/dl [47]. These dielectric parameters are mathematically derived using Eq. (4.2) utilizing the parameter values given in [110] in the sub-THz band. It is observed from the figure that permittivity values decrease with an increase in both frequency and glucose concentration due to the increase in τ1 value with an increase in concentration. Figure 4.8a and b provide a three-dimensional plot illustrating the details of the variation of

Fig. 4.7 Dielectric properties of different concentration glucose samples in 110–170 GHz band. a Real part. b Imaginary part of permittivity [47]. Reprinted with permission from IEEE

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Fig. 4.8 Dielectric properties of aqueous glucose solution with a real part, b imaginary part of permittivity as a function of frequency, t1 relaxation, and concentration of glucose in water medium

permittivity’s real and imaginary parts with increasing frequency, τ1 , and glucose concentration levels. These dielectric parameters decrease with all increase in three parameters (frequency, concentration, and τ1 ) in the 0.1–0.17 THz band.

4.4 Tissue Phantom Models for Glucose Concentration Measurements The term “phantom” refers to a sample whose geometry and materials are known and which is commonly used by scientists to develop and characterize measurement systems and algorithms [116]. The following sub-sections will focus on the detailed discussion on the development and use of the tissue-mimicking phantoms, followed by the development of blood phantoms for testing the efficiency, sensitivity, and reliability of the blood glucose measuring sensors with changes in glucose concentration.

4.4.1 Tissue-Mimicking Phantoms Tissue-modeling phantoms simulate biological tissue for the purpose of providing more clinically realistic environments. These phantoms have proven to be useful in all forms of electromagnetic wave frequencies, including X-rays and radio waves. The physical properties of tissue-mimicking phantoms vary with the modality. Within the MW domain, for example, they mimic the dielectric properties of human and animal soft and hard tissues and those of cultured cells and blood. As a result of the relative lack of equipment and facilities required for testing living animals, education and research activities in the biomedical area often rely on such test objects. In general,

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111

tissue-mimicking phantoms can be classified into two broad categories based on the environment: Physical phantoms: Physical phantoms are used for testing the performance of the instrument. In each modality, the properties of the phantoms are different. Phantoms developed for validating ultrasound testing setups mimic the acoustic properties of tissues [117–119], while MW [120–122] and sub-THz [123–126] based phantoms work to mimic the electrical properties at a particular frequency. Additionally, they are used to calibrate the instrument and ensure that its performance is stable and reproducible. The unstable properties of biological tissues make interlaboratory comparisons and standardization of equipment difficult. Therefore, reproducible and stable phantoms are developed for lab-to-lab comparisons. Numerical phantoms: Numerical phantoms are the electromagnetic models of the tissues which are used to validate physical models using simulations [128]. These phantoms help the researchers visually analyze EM waves’ propagation inside the tissues, which is not possible using the physical phantoms. On the other hand, unlike physical phantoms, numerical phantoms cannot match physical measurements due to ignoring other environmental and biophysical factors. Various voxel-based numerical phantoms have been studied in MW, sub-THz, and THz domains for electromagnetic characterization of tissues using software like ANSYS High-Frequency Structure Simulator (HFSS) [129, 130], Computer Simulation Technology (CST) Microwave Studio [131–133] and Finite Difference Time Domain (FDTD) [128, 134, 135]. Figure 4.9 shows examples of physical and numerical phantoms [127].

4.4.2 Phantoms for Non-invasive Glucose Concentration Analysis EM waves are first tested against changes in glucose concentration by substituting water for blood tissue. According to some studies, this was justified by the fact that blood plasma is a water-containing tissue and, since the mineral percentages in blood plasma are so low, the minerals can be ignored [86, 98, 136]. It is possible to investigate glucose-dependent dielectric properties by combining incremental concentrations of glucose particles in deionized water since it is free of other interactions and can only quantify glucose effects [137, 138]. Some studies have also suggested the use of a “physiological solution” (0.9% NaCl solution) to mimic blood that can be used as a basis for measuring changes in dielectric properties with respect to glucose levels [81, 93]. More complex blood vessel phantoms can be prepared by including other biomolecules like ascorbic acid, uric acid, and cholesterol to evaluate the performance of the non-invasive glucose measurement device in the presence of other biomolecules [19]. We saw in the previous section that the penetration depth decreases with the increase in the frequency of the sample. Hence, the feasibility study of sub-THz

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Fig. 4.9 a Physical phantom model of breast filled with a fibro glandular tissue-mimicking liquid developed for MRI system evaluation. b Numerical breast phantom model with a cross-section highlighting adipose and fibro glandular tissues [127]. Reprinted with permission from IEEE

systems for non-invasive glucose measurement includes developing thin tissues like earlobes and thumb-index web-space where the thickness varies from 2.5–4 mm [95, 139]. For example, Fig. 4.10 shows the geometry configuration of the phantom earlobe model developed by Xiao et al. [95] in the Ultra-wideband (UWB) MW band. The earlobe phantom consists of a 2 mm-thick blood vessel layer sandwiched between two fat layers (thickness= 2 mm) beneath the skin layers with 2 mm-thickness. The outside of the skin layer is the air portion. Similarly, we used numerical and physical thumb-index, web-space phantom model in our work to verify the sub-THz-based setup’s non-invasive characteristic (0.11–0.17 THz). The model consisted of a sample holder filled with aqueous glucose solution, as shown in Fig. 4.11a–b [47]. The proposed phantom model mimics the skin area between the thumb and the index finger (thumb-index web-space) containing blood vessels. The physical phantom (Fig. 4.11a) consisted of a sample holder made of Highdensity polyethylene (HDPE), a thermally stable polymer whose dielectric property is minimally impacted by the temperature and humidity of the surrounding environment, which mimics the two layers of skin tissue. The thickness of the sample holder is 5 mm with a wall thickness of 1 mm that holds a total volume of 0.4 mL of an aqueous solution, which resembles the thumb-index web-space skin size, which ranges from 3–6mm [140]. Different concentrations of water-glucose solutions were

4.4 Tissue Phantom Models for Glucose Concentration Measurements

113

Fig. 4.10 Earlobe phantom model consisting of 2 mm thick skin, fat and blood vessel layers [95]. Reprinted with permission from IEEE

Fig. 4.11 Physical phantoms of a thumb-Index Web-space phantom, b different concentrations of aqueous glucose samples placed in the sample holder mimicking the Thumb-Index Web-space region of human palm, and c Numerical phantom of two layers of skins sandwiching the blood vessel layer along with the experimental setup for testing non-invasive glucose detection in the sub-THz band [47]. ) Reprinted with permission from IEEE

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used to simulate the blood glucose levels between the two skin layers to determine the blood glucose concentration. The numerical model (Fig. 4.11c) consisted of two layers of skin phantoms mimicking the dielectric properties of skin tissue in the sub-THz region. The blood vessel phantoms with different glucose concentrations were simulated using different Debye parameters for different concentrations of blood (Fig. 4.8). The numerical model was used to visually analyze EM waves’ propagation inside the tissues by performing EM simulations in a 3D EM solver of the High-Frequency Structure Simulator (ANSYS HFSS).

4.5 Non-invasive Sub-Terahertz Glucose Concentration Measurement Setup Based on the results of the ex-vivo studies done on glucose levels in the blood [141, 142], the sub-THz and THz spectra have shown some promise in developing in-vivo glucose sensors. Glucose non-invasive measurement setups can be subdivided into two categories based on the reflection or transmission of the waves, as we will discuss next.

4.5.1 Measurement Using Reflection Properties of Sub-Terahertz Wave Attenuated Total Reflection (ATR) is a sampling technique utilized in conjunction with Terahertz spectroscopy, enabling samples to be directly inspected in solid or liquid form. ATR uses the property of total internal reflection to produce an evanescent wave [143]. Figure 4.12a shows the basic principle of the ATR technique. When a beam of light is passed through the ATR crystal, it is reflected at least once off the internal surface of the crystal. An evanescent wave is formed in the sample by this reflection. Variations in the incidence angle affect the amount of reflection. A detector then collects the beam at the exit of the crystal. When the incidence angle exceeds the “critical” angle, internal reflection occurs. In this case, the angle of refraction is determined by the real parts of the refractive indices of the ATR crystal and the sample as: θC = sin−1



n2 n1

 (4.3)

Here, n1 and n2 are the refractive indices of the sample and crystal, respectively. The evanescent effect only occurs when the crystal is made from an optical material with a higher refractive index than the sample being examined. Otherwise, light is

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115

Evanescent Wave

Sample

Penetration Depth

n2 n1 θ

ATR Crystal

Incident wave from Source

Reflected wave to detector

(a)

(b) Fig. 4.12 a Basic principle of ATR technology. b Commercially available ATR module developed by Teraview Ltd. (courtesy: Teraview)

lost in the sample. The depth of penetration depends on the n1 , n2, and wavelength (λ) of the light as is given by: δ=



λ

2π n 1 sin θ − 2

 2

(4.4)

n1 n2

Figure 4.12b shows the commercial ATR module by Teraview Ltd [144]. The ATR measurement is done by calculating the reflection from the free base of

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the crystal, which is used as the reference signal E r (ω) and the second reflection from the sample placed on the crystal—E s (ω) [74, 145]. The ATR amplitude is given by |Rp (ω)| = |E s (ω)|/|E r (ω)| and the phase spectra are calculated as Phase(ω) = −arg(E s (ω)/E r (ω)). The complex dielectric function ε(ω) can be calculated from Rp (ω) using the following equation [74]:  ε=

1+Rp 1−Rp

2

±



1+Rp 1−Rp

4



− sin2 (2θ )

1+Rp 1−Rp

2 (4.5)

2cos2 θ

This technique works well above 0.2 THz and has an artifactual increase of reflection amplitude at frequencies below 0.2 THz due to weakly focused THz beam [74]. Based on reflection measurements, THz ATR spectroscopy has demonstrated the ability to measure in vivo blood glucose levels non-invasively [74]. Six healthy subjects were monitored using the THz reflected signals from their palms to investigate the correlation between blood glucose concentration and ATR amplitude.

4.5.2 Measurement Using Transmission Properties of Sub-Terahertz Wave As mentioned in Chap. 2, the photonics-based sub-THz measurement setups can be bulky, and the laser efficiency decreases in the low-THz region. Hence, electronicsbased, non-invasive glucose measurement has been conducted using VNA and waveguide probe setups [47]. Figure 4.13 shows the diagrammatic representation of the measurement setup, which consists of two WR 6.5 waveguide probe sensors connected to Keysight VNA (N5247B) using the frequency extenders (Virginia Diodes, Inc., WR 6.5). Thumb-index web-space phantom model is used to detect the glucose concentration in an aqueous medium non-invasively. As shown in Fig. 4.13, the waveguide probes are placed in contact with the phantom. Sub-THz wave is propagated through the phantom model, and the transmitted and reflected waves are measured in the form of S-parameters (S 21 and S 11 ) using the waveguide probes. The S 21 parameters acquired from the waveguide probes are a function of the frequency of operation, concentration, and sample thickness. For plastic holder which resembles the skin layer of thumb-index web-space (l=1 mm) and glucose samples which correlates to human blood vessels of thickness d=3 mm, the sensitivity of the transmission parameter (dS21 ) is given by [98]: dS21 (dB)(freq = 110GHz; d = 3mm; l = 1mm) =

∂ S21 c ∂c

(4.6)

For an incremental change of concentration (c) of 15 mg/dl, dS21 is 1.8 dB. As per the technical datasheet, the Keysight VNA (N5247B) and the frequency extenders (Virginia Diodes, Inc., WR 6.5) provide a dynamic range of 110 dB along with a

4.5 Non-invasive Sub-Terahertz Glucose Concentration Measurement Setup

117

Fig. 4.13 Sub-THz transmission based non-invasive glucose measurement setup consisting of thumb-index web-space phantom for determination of aqueous glucose concentration [47]. Reprinted with permission from IEEE

measurement stability of 0.25 dB. Thus, the measurements’ uncertainty is much less than the calculated sensitivity, ensuring SU’s stability. It is worthwhile to state that the sensitivity of the proposed sensor system increases with a decrease in dimensions of waveguide [146] (increasing the frequency of operation), but at the same time, the penetration depth of the sensor system decreases, making the sensing technique unsuitable for non-invasive determination of the glucose concentration. The measured amplitude and phase of S 21 parameters in the 0.1–0.17 THz region are shown in Fig. 4.14a and b, respectively. It is evident from Fig. 4.14a that the amplitude follows an increasing trend with an increase in glucose concentration, which is attributed to the decrease in the real and imaginary parts of the permittivity. The average change of 2 dB is observed for 15 mg/dl change in concentration, satisfying the measurement system’s sensitivity criteria. The magnitude of S 21 for all concentrations lies between −50 and −90 dB, which lies in the 110 dB dynamic range for the VNA provided by the Keysight datasheet. The phase plot of the S 21 parameter depicted in Fig. 4.14b shows a decreasing trend with an increase in concentration

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4 Non-invasive Sub-Terahertz Blood Glucose Measurement -40

0

water 55 mg/dl 70 mg/dl 85 mg/dl 100 mg/dl 115 mg/dl 130 mg/dl

-50

S21 (Degree)

S21 (dB)

-100

2 dB

-60 -70 -80 -90 -100 -110 110

water 55 mg/dl 70 mg/dl 85 mg/dl 100 mg/dl 115 mg/dl 130 mg/dl

120

-200

2.5 deg -300

130

140

150

160

-400 110

170

120

130

Frequency (GHz)

140

150

160

170

Frequency (GHz)

(a)

(b)

Fig. 4.14 Measured a amplitude and b phase of S 21 parameters for various glucose concentrations in 110–170 GHz band [47]. Reprinted with permission from IEEE

which is accounted for by the decrease in electrical length with the decrease in the dielectric constant. A similar kind of amplitude and phase plot variation with change in concentration is observed for the S 11 parameter as shown in Fig. 4.15a and b, where 1 dB change is observed in the amplitude with 15 mg/dl change in glucose level. In addition, all the measurements are repeated 20 times for each concentration for repeatability verification. Figure 4.16 depicts the S 21 amplitude for 20 readings of 70 mg/dl glucose concentration. The maximum variation of 0.07 dB is observed between the readings demonstrating the effect of the system’s noise in the measurements, which is much less than the system’s sensitivity. This sub-THz sensor provides an advantage of sensitivity over mmW and MW frequency-domain sensors by providing a sensitivity of 2 dB for a 15 mg/dl increase 0

50

-10

0

1 dB

S11 (Degree)

S11 (dB)

-20 -30 -40 -50 -60 110

water 55 mg/dl 70 mg/dl 85 mg/dl 100 mg/dl 115 mg/dl 130 mg/dl

120

water 55 mg/dl 70 mg/dl 85 mg/dl 100 mg/dl 115 mg/dl 130 mg/dl

-50 -100 -150

1 deg

-200 -250

130

140

150

Frequency (GHz)

(a)

160

170

-300 110

120

130

140

150

160

170

Frequency (GHz)

(b)

Fig. 4.15 Measured a amplitude and b phase of S 11 parameters for various glucose concentrations in 110–170 GHz band [47]. Reprinted with permission from IEEE

4.6 Conclusion

119

-55

sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9 sample 10 sample 11 sample 12 sample 13 sample 14 sample 15 sample 16 sample 17 sample 18 sample 19 sample 20

-60 -65 S21 (dB)

-70 -75 -80 -85 -90 -95 -100 110

120

130

140 150 160 Frequency (GHz)

170

Fig. 4.16 S21 parameter amplitude for 70 mg/dl glucose concentration for 20 readings verifying the reproducibility of the measurement setup [47] Reprinted with permission from IEEE

in glucose concentration which lies within the clinical limits specified by ISO 15197 standards. However, as pointed out earlier, the sub-THz waves can penetrate through thin human body parts like the earlobe and thumb-index web-space. It is also interesting to note that the output power level of VNA is 9 dBm which, when exposed to human skin for a fraction of seconds, does not harm human tissues. Based on this preliminary evaluation of aqueous glucose concentration, we can expand the study to evaluate more complex blood vessel simulations, including biomolecules such as ascorbic acid, uric acid, and cholesterol, and determine how transmission and reflection parameters change. In Chap. 7, we will learn how to integrate this sensor with machine learning to enhance the measurement system’s readability by forming a non–linear relationship between S-parameters and glucose concentration values.

4.6 Conclusion In this chapter, the aim was to discuss the steps involved in developing non-invasive blood glucose measurement systems and the advantages of sub-THz and THz over other electromagnetic glucose measurement techniques illustrated with examples from the literature. Several guidelines and parameters were discussed concerning how to develop devices to measure blood glucose levels. Additionally, we discussed

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4 Non-invasive Sub-Terahertz Blood Glucose Measurement

various non-invasive glucose measurement methods using the non-ionizing electromagnetic spectrum in the electromagnetic domain research literature. The techniques were classified according to the parametric estimation of blood/tissue properties and intrinsic glucose properties; we learned that the lower spectrum focuses on intrinsic glucose properties while the non-ionizing EM spectrum uses parametric estimation to estimate blood glucose levels. The efficiency and sensitivity of subTHz glucose measuring systems were also studied using the dielectric properties of aqueous glucose solutions in the sub-THz region by using the complex permittivity variation with glucose concentration using the double Debye model. Furthermore, we discussed the importance and use of tissue-mimicking phantom modeling as a means of validating these non-invasive monitoring devices for blood glucose levels.

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Chapter 5

Breast Tumor Margin Assessment Using Sub-Terahertz Wave

Abstract According to consensus guidelines for patient management, breastconserving surgery (BCS) can be a good treatment option for early-stage cancers if the resected tissue margins are free of histological abnormalities. About 15–35% of patients encounter malignant cells at the margins or close to the original resection specimen, requiring re-surgery. The clinical findings that inadequate margins are frequent in patients who undergo surgical resection of tumors suggest that current intraoperative methods are insufficient for determining margin status. The clinical need for intraoperative margin assessment has led to the development of many new imaging techniques. Sections 5.1, 5.2, and 5.3 will describe new imaging techniques for intraoperative margin assessment in surgical oncology, broadly divided into three categories: pathology, nuclear medicine, and electromagnetic imaging. We will examine their technical properties, clinical applicability, and diagnostic properties in these sections. As we proceed through Sects. 5.4 and 5.5, we will explore how sub-THz and THz waves can be used in this application. A study of the dielectric properties of adipose, fibrous, and tumor tissues in these regions will be covered in Sect. 5.4, and the development of phantoms of these tissues for evaluation of the margin assessment setups using the THz spectrum. Our discussion in Sect. 5.5 will focus on the newly developed sub-THz/THz TPI and Si Integrated Circuit technologies to provide high-resolution images of the excised tissue for invasive ductal carcinomas (IDCs). Additionally, we will test the possibility of using sub-THz waves to detect positive and negative margins in ductal carcinoma in situ (DCIS), as well as IDCs using waveguide iris probes.

5.1 Breast-Conserving Surgery According to the American Cancer Society report, 1 in 8 women is diagnosed with invasive breast cancer, the second leading cause of death in women due to cancer after lung cancer [1, 2]. There were approximately 2.3 million new breast cancer cases in 2020, and there were 685,000 deaths [3]. Breast cancer is the most common cancer in women in the world. There are 7.8 million women who have been diagnosed with © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. K. Koul and P. Kaurav, Sub-Terahertz Sensing Technology for Biomedical Applications, Biological and Medical Physics, Biomedical Engineering, https://doi.org/10.1007/978-981-19-3140-6_5

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it in the past five years. Screening for breast cancer regularly reduces the risk of the disease by 47% compared to not screening [4]. Breast cancer deaths have declined since 1990 thanks to early detection, better screening, increased awareness, and new treatment options. Even though early diagnosis and better treatment have reduced the mortality rate in many countries, breast cancer still remains a significant health issue. Consequently, breast cancer care must be improved and optimized globally. There are two main types of breast cancer: non-invasive and invasive. Both develop from the terminal ductal lobule. Histological assessment of the morphological features of a tumor is combined with immunohistochemical (IHC) analysis to classify breast cancer [5]. Cancer in situ, also known as non-invasive breast cancer, occurs when there is no evidence of a break in the basement membrane. The cancer is therefore contained within the ductal lobular units without invading the stroma. The most common form of non-invasive breast cancer, DCIS, starts within the duct system (Fig. 5.1a). Although DCIS is typically asymptomatic, some patients may experience a detectable lump. A DCIS is classified into three categories: low-grade, intermediate-grade, and high-grade, with the latter representing the most aggressive form of cancer with the highest potential for progression to invasive cancer, as shown in Fig. 5.1b. During DCIS, microcalcifications form in the dead, stacked-up cells. DCIS is compounded by its impalpable nature—a large part of what makes it so difficult to identify both preoperatively and intraoperatively [5]. As cancer cells accumulate over time, they can migrate to the stroma of the breast, which can then lead to tumors. The typical characteristics of invasive carcinomas are firm/hard lesions that are palpable [6]. IDC is the most common form of invasive Normal Duct

Ductal Carcinoma in Situ

Invasive Carcinoma

Intermediate Grade

High Grade

(a)

Low Grade

(b) Fig. 5.1 Different a types and b grades of breast cancer

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breast cancer (Fig. 5.1a). Breast cancer patients with IDC account for 40–75% of all cases. Other types of invasive breast tumors include Invasive lobular carcinoma (ILC), accounting for 5–15% of invasive breast tumors, medullary carcinoma (1– 7%), tubular carcinoma (