Table Of ContentBreast Image Reconstruction
and Cancer Detection Using
Microwave Imaging
Online at: https://doi.org/10.1088/978-0-7503-2592-9
Breast Image Reconstruction
and Cancer Detection Using
Microwave Imaging
Hardik N Patel
Assistant Professor, ICT, Pandit Deendayal Energy University, India
Deepak K Ghodgaonkar
ProfessoratDhirubhaiAmbaniInstituteofInformationandCommunicationTechnology,
India
Jasjit S Suri
AtheroPoint LLC, Roseville, CA, USA
IOP Publishing, Bristol, UK
ªIOPPublishingLtd2022
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ISBN 978-0-7503-2592-9(ebook)
ISBN 978-0-7503-2590-5(print)
ISBN 978-0-7503-2593-6(myPrint)
ISBN 978-0-7503-2591-2(mobi)
DOI 10.1088/978-0-7503-2592-9
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Contents
Preface xiii
Acknowledgements xiv
Author biographies xv
1 Introduction to breast cancer 1-1
1.1 Introduction to cancer 1-1
1.2 Worldwide cancer statistics 1-2
1.3 Breast cancer statistics 1-3
1.3.1 Breast cancer prediction 1-7
1.4 Breast anatomy and breast cancer 1-10
1.5 Summary 1-14
References 1-14
2 Introduction to breast cancer detection techniques 2-1
2.1 Imaging modalities for breast cancer screening 2-1
2.2 Mammography 2-2
2.2.1 History of mammography 2-3
2.2.2 Basic understanding of mammography 2-4
2.2.3 Advantages and disadvantages of mammography 2-6
2.3 Ultrasound imaging 2-6
2.3.1 History of ultrasound 2-7
2.3.2 Physics of ultrasound 2-8
2.3.3 Current status of ultrasound imaging 2-8
2.3.4 Advantages and disadvantages of ultrasound 2-8
2.4 Magnetic resonanace imaging 2-9
2.4.1 Short history of MRI 2-9
2.4.2 Working principle of MRI 2-10
2.4.3 Advantages and disadvantages of MRI 2-11
2.5 Positron emission tomography 2-11
2.5.1 Short history of PET 2-12
2.5.2 Advantages and disadvantages of PET 2-12
2.6 Diffuse optical tomography 2-12
2.6.1 Short history of optical tomography 2-12
2.6.2 Advantages and disadvantages of diffuse optical 2-13
tomography
vii
BreastImageReconstructionandCancerDetectionUsingMicrowaveImaging
2.7 Electrical impedance tomography 2-13
2.7.1 Advantages and disadvantages of EIT 2-13
2.8 Computed tomography (CT) 2-13
2.8.1 Short history of CT 2-13
2.8.2 Advantages and disadvantages of CT 2-14
2.9 Microwave imaging 2-14
2.9.1 Passive microwave imaging 2-14
2.9.2 Active microwave imaging 2-14
2.10 Comparison of mammography, MRI and ultrasound 2-15
2.11 Overview of image reconstruction methods 2-15
2.11.1 Algebraic reconstruction 2-15
2.11.2 Analytic reconstruction 2-17
2.11.3 Statistical reconstruction 2-17
2.11.4 Learned iterative reconstruction 2-18
2.12 Summary 2-18
References 2-18
3 Introduction to microwave imaging 3-1
3.1 Introduction 3-1
3.2 Introduction to passive microwave imaging 3-2
3.2.1 Emission principles 3-2
3.2.2 Radiative transfer 3-3
3.2.3 Bio-heat transfer 3-4
3.2.4 Temperature resolution 3-4
3.3 Microwave radiometry for cancer detection 3-5
3.3.1 Multiprobe radiometric imaging 3-7
3.3.2 Multi-frequency microwave radiometry 3-11
3.4 Active microwave imaging 3-12
3.5 Summary 3-15
References 3-15
4 Finite difference time domain method for microwave 4-1
breast imaging
4.1 Overview of computational electromagnetic methods 4-1
4.1.1 Low frequency methods 4-1
4.1.2 High frequency methods 4-3
4.2 Motivation 4-3
viii
BreastImageReconstructionandCancerDetectionUsingMicrowaveImaging
4.3 Overview of FDTD 4-4
4.4 Derivation of basic FDTD update equations 4-5
4.5 Polarization current density equation derivation for numerical 4-14
breast phantom region
4.6 Electric field update equation derivation for numerical breast 4-17
phantom region
4.7 Derivation of electric field update equations for PML region 4-19
4.8 Magnetic field update equations 4-22
4.9 Steps for FDTD implementation 4-24
4.10 Simulation parameters 4-24
4.11 Results 4-24
4.12 Summary 4-26
References 4-29
5 3D level set based optimization 5-1
5.1 Multiple frequency inverse scattering problem formulation 5-1
5.2 Introduction 5-1
5.3 Problem formulation 5-2
5.4 Review of previous work 5-4
5.5 Theoretical foundations 5-9
5.5.1 Evolution approach 5-11
5.5.2 Optimization approach 5-12
5.6 Single 3D level set function based optimization 5-13
5.7 Two 3D level set function based optimization 5-15
5.7.1 3D level set based regularized optimization 5-18
5.7.2 Steps for 3D level set based optimization implementation 5-20
5.8 Simulation parameters 5-20
5.9 Results 5-21
5.10 Summary 5-27
References 5-29
6 Method of moments 6-1
6.1 Theoretical background 6-1
6.2 Problem formulation 6-6
6.3 Computation reduction using group theory 6-8
6.3.1 Human breast models 6-8
6.3.2 Symmetry exploitation using group theory 6-10
ix
