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Author Seo, Jin Keun.

Title Nonlinear inverse problems in imaging / Jin Keun Seo, Eung Je Woo
Published Chichester : Wiley, [2013]
©2013
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Contents 880-01 Signal and System as Vectors -- Basics of Forward Problem -- Analysis for Inverse Problem -- Numerical Methods -- CT, MRI and Image Processing Problems -- Electrical Impedance Tomography -- Anomaly Estimation and Layer Potential Techniques -- Magnetic Resonance Electrical Impedance Tomography -- Magnetic Resonance Elastography
880-01 Machine generated contents note: 1. Introduction -- 1.1. Forward Problem -- 1.2. Inverse Problem -- 1.3. Issues in Inverse Problem Solving -- 1.4. Linear, Nonlinear and Linearized Problems -- References -- 2. Signal and System as Vectors -- 2.1. Vector Spaces -- 2.1.1. Vector Space and Subspace -- 2.1.2. Basis, Norm and Inner Product -- 2.1.3. Hilbert Space -- 2.2. Vector Calculus -- 2.2.1. Gradient -- 2.2.2. Divergence -- 2.2.3. Curl -- 2.2.4. Curve -- 2.2.5. Curvature -- 2.3. Taylor's Expansion -- 2.4. Linear System of Equations -- 2.4.1. Linear System and Transform -- 2.4.2. Vector Space of Matrix -- 2.4.3. Least-Squares Solution -- 2.4.4. Singular Value Decomposition (SVD) -- 2.4.5. Pseudo-inverse -- 2.5. Fourier Transform -- 2.5.1. Series Expansion -- 2.5.2. Fourier Transform -- 2.5.3. Discrete Fourier Transform (DFT) -- 2.5.4. Fast Fourier Transform (FFT) -- 2.5.5. Two-Dimensional Fourier Transform -- References -- 3. Basics of Forward Problem -- 3.1. Understanding a PDE using Images as Examples -- 3.2. Heat Equation -- 3.2.1. Formulation of Heat Equation -- 3.2.2. One-Dimensional Heat Equation -- 3.2.3. Two-Dimensional Heat Equation and Isotropic Diffusion -- 3.2.4. Boundary Conditions -- 3.3. Wave Equation -- 3.4. Laplace and Poisson Equations -- 3.4.1. Boundary Value Problem -- 3.4.2. Laplace Equation in a Circle -- 3.4.3. Laplace Equation in Three-Dimensional Domain -- 3.4.4. Representation Formula for Poisson Equation -- References -- Further Reading -- 4. Analysis for Inverse Problem -- 4.1. Examples of Inverse Problems in Medical Imaging -- 4.1.1. Electrical Property Imaging -- 4.1.2. Mechanical Property Imaging -- 4.1.3. Image Restoration -- 4.2. Basic Analysis -- 4.2.1. Sobolev Space -- 4.2.2. Some Important Estimates -- 4.2.3. Helmholtz Decomposition -- 4.3. Variational Problems -- 4.3.1. Lax-Milgram Theorem -- 4.3.2. Ritz Approach -- 4.3.3. Euler-Lagrange Equations -- 4.3.4. Regularity Theory and Asymptotic Analysis -- 4.4. Tikhonov Regularization and Spectral Analysis -- 4.4.1. Overview of Tikhonov Regularization -- 4.4.2. Bounded Linear Operators in Banach Space -- 4.4.3. Regularization in Hilbert Space or Banach Space -- 4.5. Basics of Real Analysis -- 4.5.1. Riemann Integrability -- 4.5.2. Measure Space -- 4.5.3. Lebesgue-Measurable Function -- 4.5.4. Pointwise, Uniform, Norm Convergence and Convergence in Measure -- 4.5.5. Differentiation Theory -- References -- Further Reading -- 5. Numerical Methods -- 5.1. Iterative Method for Nonlinear Problem -- 5.2. Numerical Computation of One-Dimensional Heat Equation -- 5.2.1. Explicit Scheme -- 5.2.2. Implicit Scheme -- 5.2.3. Crank-Nicolson Method -- 5.3. Numerical Solution of Linear System of Equations -- 5.3.1. Direct Method using LU Factorization -- 5.3.2. Iterative Method using Matrix Splitting -- 5.3.3. Iterative Method using Steepest Descent Minimization -- 5.3.4. Conjugate Gradient (CG) Method -- 5.4. Finite Difference Method (FDM) -- 5.4.1. Poisson Equation -- 5.4.2. Elliptic Equation -- 5.5. Finite Element Method (FEM) -- 5.5.1. One-Dimensional Model -- 5.5.2. Two-Dimensional Model -- 5.5.3. Numerical Examples -- References -- Further Reading -- 6. CT, MRI and Image Processing Problems -- 6.1. X-ray Computed Tomography -- 6.1.1. Inverse Problem -- 6.1.2. Basic Principle and Nonlinear Effects -- 6.1.3. Inverse Radon Transform -- 6.1.4. Artifacts in CT -- 6.2. Magnetic Resonance Imaging -- 6.2.1. Basic Principle -- 6.2.2. k-Space Data -- 6.2.3. Image Reconstruction -- 6.3. Image Restoration -- 6.3.1. Role of p in (6.35) -- 6.3.2. Total Variation Restoration -- 6.3.3. Anisotropic Edge-Preserving Diffusion -- 6.3.4. Sparse Sensing -- 6.4. Segmentation -- 6.4.1. Active Contour Method -- 6.4.2. Level Set Method -- 6.4.3. Motion Tracking for Echocardiography -- References -- Further Reading -- 7. Electrical Impedance Tomography -- 7.1. Introduction -- 7.2. Measurement Method and Data -- 7.2.1. Conductivity and Resistance -- 7.2.2. Permittivity and Capacitance -- 7.2.3. Phasor and Impedance -- 7.2.4. Admittivity and Trans-Impedance -- 7.2.5. Electrode Contact Impedance -- 7.2.6. EIT System -- 7.2.7. Data Collection Protocol and Data Set -- 7.2.8. Linearity between Current and Voltage -- 7.3. Representation of Physical Phenomena -- 7.3.1. Derivation of Elliptic PDE -- 7.3.2. Elliptic PDE for Four-Electrode Method -- 7.3.3. Elliptic PDE for Two-Electrode Method -- 7.3.4. Min-Max Property of Complex Potential -- 7.4. Forward Problem and Model -- 7.4.1. Continuous Neumann-to-Dirichlet Data -- 7.4.2. Discrete Neumann-to-Dirichlet Data -- 7.4.3. Nonlinearity between Admittivity and Voltage -- 7.5. Uniqueness Theory and Direct Reconstruction Method -- 7.5.1. Calderon's Approach -- 7.5.2. Uniqueness and Three-Dimensional Reconstruction: Infinite Measurements -- 7.5.3. Nachmann's D-bar Method in Two Dimensions -- 7.6. Back-Projection Algorithm -- 7.7. Sensitivity and Sensitivity Matrix -- 7.7.1. Perturbation and Sensitivity -- 7.7.2. Sensitivity Matrix -- 7.7.3. Linearization -- 7.7.4. Quality of Sensitivity Matrix -- 7.8. Inverse Problem of EIT -- 7.8.1. Inverse Problem of RC Circuit -- 7.8.2. Formulation of EIT Inverse Problem -- 7.8.3. Ill-Posedness of EIT Inverse Problem -- 7.9. Static Imaging -- 7.9.1. Iterative Data Fitting Method -- 7.9.2. Static Imaging using Four-Channel EIT System -- 7.9.3. Regularization -- 7.9.4. Technical Difficulty of Static Imaging -- 7.10. Time-Difference Imaging -- 7.10.1. Data Sets for Time-Difference Imaging -- 7.10.2. Equivalent Homogeneous Admittivity -- 7.10.3. Linear Time-Difference Algorithm using Sensitivity Matrix -- 7.10.4. Interpretation of Time-Difference Image -- 7.11. Frequency-Difference Imaging -- 7.11.1. Data Sets for Frequency-Difference Imaging -- 7.11.2. Simple Difference Ft, ω2 -- Ft, ω1 -- 7.11.3. Weighted Difference Ft, ω2 -- αFt, ω1 -- 7.11.4. Linear Frequency-Difference Algorithm using Sensitivity Matrix -- 7.11.5. Interpretation of Frequency-Difference Image -- References -- 8. Anomaly Estimation and Layer Potential Techniques -- 8.1. Harmonic Analysis and Potential Theory -- 8.1.1. Layer Potentials and Boundary Value Problems for Laplace Equation -- 8.1.2. Regularity for Solution of Elliptic Equation along Boundary of Inhomogeneity -- 8.2. Anomaly Estimation using EIT -- 8.2.1. Size Estimation Method -- 8.2.2. Location Search Method -- 8.3. Anomaly Estimation using Planar Probe -- 8.3.1. Mathematical Formulation -- 8.3.2. Representation Formula -- References -- Further Reading -- 9. Magnetic Resonance Electrical Impedance Tomography -- 9.1. Data Collection using MRI -- 9.1.1. Measurement of Bz -- 9.1.2. Noise in Measured Bz Data -- 9.1.3. Measurement of B = (Bx, By, Bz) -- 9.2. Forward Problem and Model Construction -- 9.2.1. Relation between J, Bz and σ -- 9.2.2. Three Key Observations -- 9.2.3. Data Bz Traces σ u x ez Directional Change of σ -- 9.2.4. Mathematical Analysis toward MREIT Model -- 9.3. Inverse Problem Formulation using B or J -- 9.4. Inverse Problem Formulation using Bz -- 9.4.1. Model with Two Linearly Independent Currents -- 9.4.2. Uniqueness -- 9.4.3. Defected Bz Data in a Local Region -- 9.5. Image Reconstruction Algorithm -- 9.5.1. J-substitution Algorithm -- 9.5.2. Harmonic Bz Algorithm -- 9.5.3. Gradient Bz Decomposition and Variational Bz Algorithm -- 9.5.4. Local Harmonic Bz Algorithm -- 9.5.5. Sensitivity Matrix-based Algorithm -- 9.5.6. Anisotropic Conductivity Reconstruction Algorithm -- 9.5.7. Other Algorithms -- 9.6. Validation and Interpretation -- 9.6.1. Image Reconstruction Procedure using
Summary "This book provides researchers and engineers in the imaging field with the skills they need to effectively deal with nonlinear inverse problems associated with different imaging modalities, including impedance imaging, optical tomography, elastography, and electrical source imaging. Focusing on numerically implementable methods, the book bridges the gap between theory and applications, helping readers tackle problems in applied mathematics and engineering. Complete, self-contained coverage includes basic concepts, models, computational methods, numerical simulations, examples, and case studies. Provides a step-by-step progressive treatment of topics for ease of understanding. Discusses the underlying physical phenomena as well as implementation details of image reconstruction algorithms as prerequisites for finding solutions to non linear inverse problems with practical significance and value. Includes end of chapter problems, case studies and examples with solutions throughout the book. Companion website will provide further examples and solutions, experimental data sets, open problems, teaching material such as PowerPoint slides and software including MATLAB m files. Essential reading for Graduate students and researchers in imaging science working across the areas of applied mathematics, biomedical engineering, and electrical engineering and specifically those involved in nonlinear imaging techniques, impedance imaging, optical tomography, elastography, and electrical source imaging"-- Provided by publisher
Bibliography Includes bibliographical references and index
Notes Print version record
Subject Cross-sectional imaging -- Mathematics.
Image processing -- Mathematics.
Inverse problems (Differential equations)
Nonlinear theories.
Form Electronic book
Author Woo, E. J. (Eung Je)
ISBN 1118478142 (electronic bk.)
1118478150
1118478177 (electronic bk.)
1283993848 (MyiLibrary)
9781118478141 (electronic bk.)
9781118478158
9781118478172 (electronic bk.)
9781283993845 (MyiLibrary)