Breast Image Reconstruction and Cancer Detection Using Microwave Imaging / Hardik N. Patel, Deepak K. Ghodgaonkar, and Jasjit S Suri.

Format:

Book

Author/Creator:

Patel, Hardik N., author.

Ghodgaonkar, Deepak K., author.

S Suri, Jasjit, author.

Series:

IOP Ebooks Series

Language:

English

Subjects (All):

Breast--Imaging.

Breast.

Physical Description:

1 online resource (263 pages)

Edition:

First edition.

Place of Publication:

Bristol, England : IOP Publishing, [2022]

Summary:

This reference text explores breast cancer, Microwave scattering and microwave imaging based cancer detection. It also covers the basics of Microwave imaging and advanced methods in image reconstruction techniques. The role of machine learning and artificial intelligence in breast cancer diagnosis is also discussed.

Contents:

Intro

Preface

Acknowledgements

Author biographies

Hardik N Patel

Deepak K Ghodgaonkar

Jasjit S Suri

Chapter 1 Introduction to breast cancer

1.1 Introduction to cancer

1.2 Worldwide cancer statistics

1.3 Breast cancer statistics

1.3.1 Breast cancer prediction

1.4 Breast anatomy and breast cancer

1.5 Summary

References

Chapter 2 Introduction to breast cancer detection techniques

2.1 Imaging modalities for breast cancer screening

2.2 Mammography

2.2.1 History of mammography

2.2.2 Basic understanding of mammography

2.2.3 Advantages and disadvantages of mammography

2.3 Ultrasound imaging

2.3.1 History of ultrasound

2.3.2 Physics of ultrasound

2.3.3 Current status of ultrasound imaging

2.3.4 Advantages and disadvantages of ultrasound

2.4 Magnetic resonanace imaging

2.4.1 Short history of MRI

2.4.2 Working principle of MRI

2.4.3 Advantages and disadvantages of MRI

2.5 Positron emission tomography

2.5.1 Short history of PET

2.5.2 Advantages and disadvantages of PET

2.6 Diffuse optical tomography

2.6.1 Short history of optical tomography

2.6.2 Advantages and disadvantages of diffuse optical tomography

2.7 Electrical impedance tomography

2.7.1 Advantages and disadvantages of EIT

2.8 Computed tomography (CT)

2.8.1 Short history of CT

2.8.2 Advantages and disadvantages of CT

2.9 Microwave imaging

2.9.1 Passive microwave imaging

2.9.2 Active microwave imaging

2.10 Comparison of mammography, MRI and ultrasound

2.11 Overview of image reconstruction methods

2.11.1 Algebraic reconstruction

2.11.2 Analytic reconstruction

2.11.3 Statistical reconstruction

2.11.4 Learned iterative reconstruction

2.12 Summary

Chapter 3 Introduction to microwave imaging

3.1 Introduction.

3.2 Introduction to passive microwave imaging

3.2.1 Emission principles

3.2.2 Radiative transfer

3.2.3 Bio-heat transfer

3.2.4 Temperature resolution

3.3 Microwave radiometry for cancer detection

3.3.1 Multiprobe radiometric imaging

3.3.2 Multi-frequency microwave radiometry

3.4 Active microwave imaging

3.5 Summary

Chapter 4 Finite difference time domain method for microwave breast imaging

4.1 Overview of computational electromagnetic methods

4.1.1 Low frequency methods

4.1.2 High frequency methods

4.2 Motivation

4.3 Overview of FDTD

4.4 Derivation of basic FDTD update equations

4.5 Polarization current density equation derivation for numerical breast phantom region

4.6 Electric field update equation derivation for numerical breast phantom region

4.7 Derivation of electric field update equations for PML region

4.8 Magnetic field update equations

4.9 Steps for FDTD implementation

4.10 Simulation parameters

4.11 Results

4.12 Summary

Chapter 5 3D level set based optimization

5.1 Multiple frequency inverse scattering problem formulation

5.2 Introduction

5.3 Problem formulation

5.4 Review of previous work

5.5 Theoretical foundations

5.5.1 Evolution approach

5.5.2 Optimization approach

5.6 Single 3D level set function based optimization

5.7 Two 3D level set function based optimization

5.7.1 3D level set based regularized optimization

5.7.2 Steps for 3D level set based optimization implementation

5.8 Simulation parameters

5.9 Results

5.10 Summary

Chapter 6 Method of moments

6.1 Theoretical background

6.2 Problem formulation

6.3 Computation reduction using group theory

6.3.1 Human breast models

6.3.2 Symmetry exploitation using group theory.

6.4 Inverse scattering problem formulation

6.5 Simulation parameters and noise consideration

6.6 Results

6.7 Summary

Chapter 7 Finite difference time domain for microwave imaging

7.1 Introduction to finite difference time domain

7.1.1 Grid size and stability

7.1.2 Input wave for Yee grid computations

7.1.3 Two-dimensional FDTD analysis of microwave breast imaging

7.1.4 Healthy breast tissue dielectric properties

7.1.5 Design of antenna array

7.2 Microwave image formation using confocal technique

7.3 Space-time beamforming

7.4 Removal of skin-breast artifact

7.5 FDTD based time reversal for microwave breast cancer detection

7.5.1 Matched filter FDTD based time reversal

7.6 Summary

Chapter 8 Review of machine learning based image reconstruction for different imaging modalities

8.1 Introduction

8.1.1 Image reconstruction (inverse) problem formulation

8.2 Traditional image reconstruction techniques

8.3 Machine learning techniques for image reconstruction

8.3.1 Machine learning based solution of inverse problems

8.3.2 Machine learning in computed tomography

8.3.3 Physics of low dose x-ray CT

8.4 Performance analysis of proposed approaches

8.5 Summary

Chapter 9 Review of machine learning based image reconstruction for microwave breast imaging

9.1 Motivation

9.2 Machine learning in microwave imaging

9.2.1 Current challenges in microwave breast diagnosis systems

9.2.2 Challenges in the development of robust machine learning classification models

9.3 Flow of the machine learning based microwave breast imaging for cancer diagnosis

9.3.1 Data collection through microwave scanning

9.3.2 Data processing

9.3.3 Diagnosis

9.4 Variational Bayesian inversion for microwave breast imaging.

9.5 Deep neural networks for microwave breast imaging

9.6 Summary

Chapter 10 Microwave image reconstruction methods

10.1 Levenberg-Marquardt method

10.1.1 Forward problem

10.1.2 Inverse problem solution by using Levenberg-Marquardt

10.1.3 Choice of the regularization parameter

10.2 Gauss-Newton method

10.2.1 Forward problem formulation

10.2.2 The inverse problem formulation

10.2.3 Gauss-Newton optimization in general

10.2.4 Gauss-Newton method for the least squares

10.2.5 The BFGS quasi-Newton method

10.3 Born iterative method

10.4 Stochastic optimization methods for microwave imaging

10.4.1 Genetic algorithm

10.5 Summary

Chapter 11 The role of AI in diagnosis, treatment and monitoring of breast cancer during COVID-19 and ahead

11.1 Introduction

11.2 AI architectures

11.3 The role of artificial intelligence in diagnosis of breast cancer

11.4 The role of AI in treatment of breast cancer

11.5 The role of AI in monitoring of breast cancer

11.6 AI based integrated system for breast cancer management

11.7 Summary

Chapter

A.1 Numerical breast phantom

A.2 Antenna placement surrounding a numerical breast phantom

A.3 Immersion (surrounding) medium

B.1 Debye model

B.2 Derivation of electric field update equations for numerical breast phantom region

B.3 Derivation of electric field update equations for PML region

B.4 Power calculations

References.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references.

ISBN:

0-7503-4381-8

OCLC:

1429725443