Breast image reconstruction and cancer detection using microwave imaging /

This reference text explores cutting edge research into the detection of breast cancer using microwave imaging.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Patel, Hardik N. (Autor), Ghodgaonkar, Deepak K. (Autor), Suri, Jasjit S. (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2022]
Colección:IOP (Series). Release 22.
IOP ebooks. 2022 collection.
Temas:
Breast > Cancer > Imaging.
Microwave imaging in medicine.
Breast Neoplasms > diagnostic imaging.
Microwave Imaging.
Biomedical engineering.
TECHNOLOGY & ENGINEERING / Biomedical.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 1. Introduction to breast cancer
  • 1.1. Introduction to cancer
  • 1.2. Worldwide cancer statistics
  • 1.3. Breast cancer statistics
  • 1.4. Breast anatomy and breast cancer
  • 1.5. Summary
  • 2. Introduction to breast cancer detection techniques
  • 2.1. Imaging modalities for breast cancer screening
  • 2.2. Mammography
  • 2.3. Ultrasound imaging
  • 2.4. Magnetic resonance imaging
  • 2.5. Positron emission tomography
  • 2.6. Diffuse optical tomography
  • 2.7. Electrical impedance tomography
  • 2.8. Computed tomography (CT)
  • 2.9. Microwave imaging
  • 2.10. Comparison of mammography, MRI and ultrasound
  • 2.11. Overview of image reconstruction methods
  • 2.12. Summary
  • 3. Introduction to microwave imaging
  • 3.1. Introduction
  • 3.2. Introduction to passive microwave imaging
  • 3.3. Microwave radiometry for cancer detection
  • 3.4. Active microwave imaging
  • 3.5. Summary
  • 4. Finite difference time domain method for microwave breast imaging
  • 4.1. Overview of computational electromagnetic 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
  • 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
  • 5.6. Single 3D level set function based optimization
  • 5.7. Two 3D level set function based optimization
  • 5.8. Simulation parameters
  • 5.9. Results
  • 5.10. Summary
  • 6. Method of moments
  • 6.1. Theoretical background
  • 6.2. Problem formulation
  • 6.3. Computation reduction using group theory
  • 6.4. Inverse scattering problem formulation
  • 6.5. Simulation parameters and noise consideration
  • 6.6. Results
  • 6.7. Summary
  • 7. Finite difference time domain for microwave imaging
  • 7.1. Introduction to finite difference time domain
  • 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
  • 7.6. Summary
  • 8. Review of machine learning based image reconstruction for different imaging modalities
  • 8.1. Introduction
  • 8.2. Traditional image reconstruction techniques
  • 8.3. Machine learning techniques for image reconstruction
  • 8.4. Performance analysis of proposed approaches
  • 8.5. Summary
  • 9. Review of machine learning based image reconstruction for microwave breast imaging
  • 9.1. Motivation
  • 9.2. Machine learning in microwave imaging
  • 9.3. Flow of the machine learning based microwave breast imaging for cancer diagnosis
  • 9.4. Variational Bayesian inversion for microwave breast imaging
  • 9.5. Deep neural networks for microwave breast imaging
  • 9.6. Summary
  • 10. Microwave image reconstruction methods
  • 10.1. Levenberg-Marquardt method
  • 10.2. Gauss-Newton method
  • 10.3. Born iterative method
  • 10.4. Stochastic optimization methods for microwave imaging
  • 10.5. Summary
  • 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
  • Appendix A. Numerical breast phantom, antenna placement and immersion (surrounding) medium
  • Appendix B. Important derivations.

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