Principles And Advanced Methods In Medical Imaging And Image Analysis 1st Edition by Atam Dhawan, Bernie Huang, Dae Shik Kim 9812705341 9789812705341
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ISBN 10: 9812705341
ISBN 13: 9789812705341
Author: Atam P. Dhawan; Bernie H K Huang; Dae-Shik Kim
Computerized medical imaging and image analysis have been the central focus in diagnostic radiology. They provide revolutionalizing tools for the visualization of physiology as well as the understanding and quantitative measurement of physiological parameters. This book offers in-depth knowledge of medical imaging instrumentation and techniques as well as multidimensional image analysis and classification methods for research, education, and applications in computer-aided diagnostic radiology. Internationally renowned researchers and experts in their respective areas provide detailed descriptions of the basic foundation as well as the most recent developments in medical imaging, thus helping readers to understand theoretical and advanced concepts for important research and clinical applications.
Table of contents:
1. Introduction to Medical Imaging and Image Analysis: A Multidisciplinary Paradigm Atam P Dhawan, H
1.1 INTRODUCTION
1.1.1 Book Chapters
Part I. Principles of Medical Imaging and Image Analysis
2. Medical Imaging and Image Formation Atam P Dhawan
2.1 INTRODUCTION
2.2 X-RAY IMAGING
2.3 MAGNETIC RESONANCE IMAGING
2.4 SINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY
2.5 POSITRON EMISSION TOMOGRAPHY
2.6 ULTRASOUND IMAGING
2.7 PRINCIPLES OF IMAGE FORMATION
2.8 RECEIVER OPERATING CHARACTERISTICS (ROC) ANALYSIS AS A PERFORMANCE MEASURE
2.9 CONCLUDING REMARKS
References
3. Principles of X-ray Anatomical Imaging Modalities Brent J Liu and HK Huang
3.1 INTRODUCTION
3.2 DIGITAL FLUOROGRAPHY
3.3 IMAGING PLATE TECHNOLOGY
3.3.1 Principle of the Laser-Stimulated Luminescence Phosphor Plate
3.3.2 Computed Radiography System Block Diagram and its Principle of Operation
3.3.3 Operating Characteristics of the CR System
3.4 FULL-FIELD DIRECT DIGITAL MAMMOGRAPHY
3.4.1 Screen/Film and Digital Mammography
3.4.2 Full Field Direct Digital Mammography
3.5 DIGITAL RADIOGRAPHY
3.6 X-RAY CT AND MULTISLICE CT
3.6.1 Image Reconstruction from Projections
3.6.1.1 The Fourier Projection Theorem
3.6.1.2 The Algebraic Reconstruction Method
3.6.1.3 The Filtered (Convolution) Back-Projection Method
3.6.2 Transmission X-ray Computed Tomography (XCT)
3.6.2.1 Conventional XCT
3.6.2.2 Spiral (Helical) XCT
3.6.2.3 Cine XCT
3.6.2.4 Multislice XCT
3.6.3 Some Standard Terminology Used in Multi-Slice XCT
3.6.4 Four-Dimensional (4D) XCT
3.6.4.1 PET/XCT Fusion Scanner
3.6.4.2 Components and Data Flow of an XCT Scanner
3.6.5 XCT Image Data
3.6.5.1 Slice Thickness
3.6.5.2 Image Data Size
3.6.5.3 Data Flow/Post-Processing
References
4. Principles of Nuclear Medicine Imaging Modalities Lionel S Zuckier
4.1 INTRODUCTION
4.1.1 Physical Basis of Nuclear Medicine
4.1.2 Conceptual Basis of Nuclear Medicine
4.1.3 Radiopharmaceuticals in Nuclear Medicine
4.2 NUCLEAR MEDICINE EQUIPMENT8
4.2.1 Nonimaging
4.2.2 The Rectilinear Scanner
4.2.3 The Anger Gamma Camera
4.2.3.1 Background
4.2.3.2 Collimation14,15
4.2.3.3 Crystal
4.2.3.4 Positioning Circuitry and Electronics
4.2.3.5 Modes of Acquisition
4.2.3.6 Analysis of Data
4.2.4 Alternate Scintigraphic Camera Designs
4.3 TOMOGRAPHY
4.3.1 Single-Photon
4.3.2 Dual-Photon
4.3.3 Fusion Imaging in Nuclear Medicine
4.4 CONCLUDING REMARKS
References
5. Principles of Magnetic Resonance Imaging Itamar Ronen and Dae-Shik Kim
5.1 PHYSICAL AND CHEMICAL FOUNDATIONS OF MRI
5.1.1 Angular Momentum of Atomic Nuclei
5.1.2 Energy States of a Nucleus with a Spin I
5.1.3 Nuclear Magnetic Moment
5.1.4 The Interaction with an External Magnetic Field
5.1.5 The Classical Picture
5.1.6 Distribution Among m States
5.1.7 Macroscopic (Bulk) Magnetization
5.1.8 The Interaction with Radiofrequency Radiation — the Resonance Phenomenon
5.2 THE BLOCH EQUATIONS
5.2.1 The Inclusion of the RF Field in the Bloch Equations
5.2.2 The Rotating Frame of Reference
5.2.3 RF Pulses
5.3 THE FREE INDUCTION DECAY
5.3.1 The NMR spectrum
5.3.2 Relaxation in NMR
5.3.2.1 T1 Relaxation
5.3.2.2 Example — the dipole-dipole interaction
5.3.2.3 T2 Relaxation
5.3.2.4 T 2 ― The Effects of Field Inhomogeneity
5.3.2.5 Refocusing the Effects of Static B0 Inhomogeneity — The Spin Echo
5.3.2.6 The Effect of T1
5.4 SPATIAL ENCODING — DIFFERENTIATING THE NMR SIGNAL ACCORDING TO ITS SPATIAL ORIGINS
5.4.1 Acquisition in the Presence of a MFG
5.4.2 MFG, Spectral Width and Field-of View
5.4.3 Another Way to Look at the Effect of MFG
5.4.4 Flexibility in Collecting Data in K-Space
5.4.5 The Gradient Echo
5.4.6 Encoding for the Third Dimension: Slice Selection or Additional Phase Encoding
5.4.7 Intraslice Phase Dispersion
5.4.8 A Complete Pulse Sequence
5.4.9 Contrast in MRI Sequences
5.4.10 Echo Planar Imaging (EPI)
5.5 CONCLUSION
References
6. Principles of Ultrasound Imaging Modalities Elisa Konofagou
6.1 INTRODUCTION
6.2 BACKGROUND
6.2.1 The Wave Equation
6.2.1.1 Impedance, Power and Re.ection
6.2.1.2 Tissue Scattering
6.2.1.3 Attenuation
6.3 KEY TOPICS WITH RESULTS AND FINDINGS
6.3.1 Transducers
6.3.2 Ultrasonic Instrumentation
6.3.2.1 Transducer Frequency
6.3.2.2 RF Amplifier
6.3.2.3 Time-Gain Compensation (TGC)
6.3.2.4 Compression Amplifier
6.3.3 Ultrasonic Imaging
6.3.3.1 A-Mode
6.3.3.2 B-Mode
6.3.3.3 M-Mode
6.4 DISCUSSION
6.5 CONCLUDING REMARKS
References
7. Principles of Image Reconstruction Methods Atam P Dhawan
7.1 INTRODUCTION
7.2 RADON TRANSFORM
7.2.1 Reconstruction with Fourier Transform
7.2.2 Reconstruction using Inverse Radon Transform
7.3 BACKPROJECTION METHOD FOR IMAGE RECONSTRUCTION
7.4 ITERATIVE ALGEBRAIC RECONSTRUCTION TECHNIQUES (ART)
7.5 ESTIMATION METHODS
7.6 CONCLUDING REMARKS
References
8. Principles of Image Processing Methods Atam P Dhawan
8.1 INTRODUCTION
8.2 IMAGE PROCESSING IN SPATIAL DOMAIN
8.2.1 Image Histogram Representation
8.2.2 Histogram Equalization
8.2.3 Histogram Modi.cation
8.2.4 Image Averaging
8.2.4.1 Neighborhood Operations
8.2.4.2 Median Filter
8.2.4.3 Adaptive Arithmetic Mean Filter
8.2.4.4 Image Sharpening and Edge Enhancement
8.3 FREQUENCY DOMAIN FILTERING
8.3.1 Inverse Filtering
8.3.2 Wiener Filtering
8.4 CONSTRAINED LEAST SQUARE FILTERING
8.4.1 Low-Pass Filtering
8.4.2 High-Pass Filtering
8.5 CONCLUDING REMARKS
References
9. Image Segmentation and Feature Extraction Atam P Dhawan
9.1 INTRODUCTION
9.2 EDGE-BASED IMAGE SEGMENTATION
9.2.1 Edge Detection Operations
9.2.1.1 Boundary Tracking
9.3 PIXEL-BASED DIRECT CLASSIFICATION METHODS
9.3.1 Optimal Global Thresholding
9.3.2 Pixel Classification Through Clustering
9.3.2.1 k-Means Clustering
9.3.2.2 Fuzzy c-Means Clustering
9.4 REGION-BASED SEGMENTATION
9.4.1 Region-growing
9.4.2 Region-splitting
9.5 RECENT ADVANCES IN SEGMENTATION
9.6 IMAGE SEGMENTATION USING NEURAL NETWORKS
9.7 FEATURE EXTRACTION AND REPRESENTATION
9.7.1 Statistical Pixel-Level Image Features
9.7.2 Shape Features
9.7.3 Moments for Shape Description
9.7.4 Texture Features
9.7.5 Hough Transform
9.8 CONCLUDING REMARKS
References
10. Clustering and Pattern Classi.cation Atam P Dhawan and Shuangshuang Dai
10.1 INTRODUCTION
10.2 DATA CLUSTERING
10.2.1 Hierarchical Clustering with the Agglomerative Method
10.2.2 Non-hierarchical or Partitional Clustering
10.2.2.1 K-Means Clustering Approach
10.2.3 Fuzzy Clustering
10.2.3.1 Fuzzy Membership Function
10.2.3.2 Membership Function Formulation
10.2.3.3 Fuzzy k-Means Clustering
10.3 NEAREST NEIGHBORED CLASSIFIER
10.4 DIMENSIONALITY REDUCTION
10.4.1 Principal Component Analysis
10.4.2 Genetic Algorithms Based Optimization
10.5 NON-PARAMETRIC CLASSIFIERS
10.5.1 Backpropagation Neural Network for Classi.cation
10.5.2 Classi.cation Using Radial Basis Functions
10.6 EXAMPLE CLASSIFICATION ANALYSIS USING FUZZY MEMBERSHIP FUNCTION
10.7 CONCLUDING REMARKS
References
Part II. Recent Advances in Medical Imaging and Image Analysis
11. Recent Advances in Functional Magnetic Resonance Imaging Dae-Shik Kim
11.1 INTRODUCTION
11.2 NEURAL CORRELATE OF fMRI
11.2.1 Do BOLD Signal Changes Re.ect the Magnitude of Neural Activity Change Linearly?
11.2.2 Small Versus Large Number
11.2.3 Relationship between Voxel Size and Neural Correspondence
11.2.4 Spiking or Subthreshold?
11.2.5 Excitatory or Inhibitory Activity?
11.3 NON-CONVENTIONAL fMRI
11.4 CONCLUSIONS AND FUTURE PROBLEMS OF fMRI
11.5 ACKNOWLEDGMENTS
References
12. Recent Advances in Diffusion Magnetic Resonance Imaging Dae-Shik Kim and Itamar Ronen
12.1 INTRODUCTION
12.1.1 Brownian Motion and Molecular Diffusion
12.1.2 Anisotropic Diffusion
12.1.3 Data Acquisition for DWI and DTI
12.1.4 Measures of Anisotropy Using Diffusion Tensors
12.1.5 White Matter Tractography
12.1.6 Propagation Algorithms
12.1.6.1 Fiber assignment by continuous tracking
12.1.6.2 Streamline tracking
12.1.6.3 Tensor deffection
12.1.6.4 Tensorline algorithms
12.1.6.5 Probabilistic mapping algorithm
12.1.7 Limitations of DTI Techniques
12.1.8 The Use of High b-value DWI for Tissue Structural Characterization
12.2 SUMMARY AND CONCLUSIONS
12.3 ACKNOWLEDGMENTS
References
13. Fluorescence Molecular Imaging: Microscopic to Macroscopic Sachin V Patwardhan, Walter J Akers a
13.1 INTRODUCTION
13.2 FLUORESCENCE CONTRAST AGENT: ENDOGENOUS AND EXOGENOUS
13.2.1 Endogenous Fluorophores
13.2.2 Exogenous Fluorophores
13.3 FLUORESCENCE IMAGING
13.3.1 Fluorescence Microscopic Imaging
13.3.2 Fluorescence Macroscopic Imaging
13.3.3 Planar Fluorescence Imaging
13.3.3.1 Fluorescence molecular tomography
13.4 CONCLUSIONS
13.5 ACKNOWLEDGMENT
References
14. Tracking Endocardium Using Optical Flow Along Iso-Value Curve Qi Duan, Elsa Angelini, Shunichi H
14.1 INTRODUCTION
14.2 MATHEMATICAL ANALYSIS
14.2.1 Optical Flow Constraint Equation
14.2.2 Optical Flow along Iso-Value Curves
14.3 METHODS AND RESULTS
14.3.1 Example I: Tracking Radial Displacements of the Endocardium in 2D Cardiac MRI Series
14.3.1.1 Mathematical analysis
14.3.1.2 Data and evaluation methods
14.3.1.3 Results
14.3.2 Example II: Tracking the Endocardium in Real-Time 3D Ultrasound
14.3.2.1 Mathematical analysis
14.3.2.2 Data and evaluation method
14.3.2.3 Results
14.3.3 Example III: Thickening Computation on 2D Ultrasound Slices
14.3.3.1 Data and method
14.3.3.2 Results
14.4 DISCUSSION
14.5 CONCLUSION
14.6 ACKNOWLEDGMENT
References
15. Some Recent Developments in Reconstruction Algorithms for Tomographic Imaging Chien-Min Kao, Emi
15.1 IMAGE RECONSTRUCTION IN COMPUTED TOMOGRAPHY
15.1.1 Introduction
15.1.2 The Data Model of Helical Cone Beam CT
15.1.3 The BPF Algorithm
15.1.4 The Long Object Problem and ROI Reconstruction
15.2 IMAGE RECONSTRUCTION IN POSITRON EMISSION TOMOGRAPHY
15.2.1 Introduction
15.2.2 Imaging Model
15.2.3 Image Reconstruction
15.2.4 Three-Dimensional Imaging, Dynamic Imaging, and List Model Reconstruction
15.3 IMAGE RECONSTRUCTION IN SINGLE PHOTON EMISSION COMPUTED TOMOGRAPHY
15.3.1 Introduction
15.3.2 No Attenuation, No Depth-dependent Resolution
15.3.3 Uniform Attenuation Alone
15.3.4 Distance-dependent Resolution Alone
15.3.6 Nonuniform Attenuation Alone
15.3.7 Short Scan and Region of Interest Imaging
15.4 ACKNOWLEDGMENTS
References
16. Shape-Based Reconstruction from Nevoscope Optical Images of Skin Lesions Song Wang and Atam P Dh
16.1 INTRODUCTION
16.2 OPTICAL IMAGING METHODS
16.2.1 Surface Imaging
16.2.2 Fluorescence Imaging
16.2.3 Optical Coherence Tomography
16.2.4 Optical Spectroscope
16.2.5 Optical Tomography
16.3 METHODOLOGY: SHAPE-BASED OPTICAL RECONSTRUCTION
16.3.1 Forward Modeling
16.3.2 Shape Representation of Skin-Lesions
16.3.3 Reconstruction Algorithm
16.3.4 Phantom and Error Evaluation
16.4 RESULTS AND DISCUSSIONS
16.5 CONCLUSION
16.6 ACKNOWLEDGMENTS
References
17. Multimodality Image Registration and Fusion Pat Zanzonico
17.1 INTRODUCTION
17.2 BACKGROUND
17.3 PROCEDURES AND METHODS
17.3.1 “Software” versus “Hardware” Approaches to Image Registration
17.3.2 Software Approaches
17.3.2.1 Rigid versus non-rigid transformations
17.3.2.2 Feature- and intensity-based approaches
17.3.2.3 Mutual information
17.3.2.4 Goodness-of-alignment metrics
17.3.3 Hardware Approaches
17.3.4 Image Fusion
17.4 RESULTS AND FINDINGS
17.4.1 Software Approaches to Image Registration
17.4.1.1 Feature-based approach: Extrinsic .duciary markers
17.4.1.2 Intensity-based approach: Minimization of intensity differences
17.4.1.3 Intensity-based approach: Matching of voxel intensity histograms
17.4.1.4 Mutual information
17.4.2 Hardware Approaches to Image Registration
17.5 DISCUSSION AND CONCLUDING REMARKS
References
18. Wavelet Transform and Its Applications in Medical Image Analysis Atam P Dhawan
18.1 INTRODUCTION
18.2 WAVELET TRANSFORM
18.3 SERIES EXPANSION AND DISCRETE WAVELET TRANSFORM
18.4 IMAGE PROCESSING USING WAVELET TRANSFORM
18.5 FEATURE EXTRACTION USING WAVELET TRANSFORM FOR IMAGE ANALYSIS
18.5.1 Feature Extraction Through Wavelet Transform
18.6 CONCLUDING REMARKS
References
19. Multiclass Classi.cation for Tissue Characterization Atam P Dhawan
19.1 INTRODUCTION
19.2 MULTICLASS CLASSIFICATION USING MAXIMUM LIKELIHOOD DISCRIMINANT FUNCTIONS
19.2.1 Maximum Likelihood Discriminant Analysis
19.3 NEURO-FUZZY CLASSIFIERS FOR MULTICLASS CLASSIFICATION
19.3.1 Convex Set Creation
19.3.1.1 Algorithm A1: Checking point B to be within convex hull (CH)
19.3.1.2 Algorithm A2: Creation of convex subsets
19.3.1.3 Initial subset point selection
19.3.1.4 Placing hyperplanes — Hyperplane layer creation
19.3.2 Fuzzy Membership Function Construction
19.3.3 Winner-Take-All Output for Classiffication
19.4 SUPPORT VECTOR MACHINE (SVM) FOR MULTICLASS CLASSIFICATION
19.5 MULTICLASS CLASSIFICATION OF MULTIPARAMETER MR BRAIN IMAGES
19.6 CONCLUDING REMARKS
References
20. From Pairwise Medical Image Registration to Populational Computational Atlases M De Craene and A
20.1 INTRODUCTION
20.1.1 Fusion of Multimodal Images
20.1.2 Atlas-Based Segmentation
20.1.3 Quantifying Temporal Deformations
20.1.4 Surgery and Preoperative Roadmap
20.1.5 Voxel-Based Morphometry
20.2 IMAGE FEATURES AND SIMILARITY METRICS
20.3 TRANSFORMATION REPRESENTATION
20.3.1 Dense Deformation Field Versus Prior Transformation Model
20.4 REGULARIZATION AND PRIOR KNOWLEDGE
20.5 ATLAS CONSTRUCTION
20.5.1 Individual Atlases
20.5.2 Probabilistic and Statistical Atlases
20.5.2.1 Probabilistic atlases
20.5.2.2 Statistical atlases
20.5.3 Alignment of an Image Population
20.6 CONCLUSION
20.7 ACKNOWLEDGMENTS
References
21. Grid Methods for Large Scale Medical Image Archiving and Analysis HK Huang, Zheng Zhou and Brent
21.1 INTRODUCTION
21.1.1 Background
21.1.2 Large-Scale Medical Imaging Systems — PACS
21.2 GRID COMPUTING FUNDAMENTALS
21.2.1 Grid Computing
21.2.2 The Globus Five-Layer Toolkit
21.2.3 Integration of DICOM with Globus
21.3 DATA GRID: LARGE-SCALE MEDICAL IMAGE MANAGEMENT SYSTEMS FOR CLINICAL SERVICES
21.3.1 Data Grid for PACS Archive and Q/R
21.3.2 Data Back Up and Disaster Recovery
21.3.2.1 The GAP
21.3.2.2 DICOM Q/R
21.3.2.3 The metadata database
21.3.3 Three Tasks of the Data Grid During the PACS Server or Archive Failure
21.4 GRID COMPUTING — COMBINING IMAGE MANAGEMENT AND ANALYSIS
21.4.1 Computational Services Architecture in the Data Grid
21.4.2 An Example of the Computing Grid — CAD of Multiple Sclerosis (MS) on MRI
21.4.2.1 Multiple sclerosis
21.4.2.2 Integration of MS CAD with data grid and grid computing
21.4.3 Integration of CAD/PACS with the Computational Services in the Data Grid
21.5 SUMMARY
21.6 ACKNOWLEDGMENTS
References
22. Image-Assisted Knowledge Discovery and Decision Support in Radiation Therapy Planning Brent J Li
22.1 INTRODUCTION
22.1.1 Need for Imaging Informatics in Radiation Therapy Planning
22.1.2 Current State of Imaging Informatics in RT
22.1.3 Review of Electronic Patient Record (EPR)
22.2 PROCEDURES AND METHODS
22.2.1 Introduction to the Medical Imaging Informatics Approach for Developing Quanti.ed Knowledge a
22.2.2 Work.ow Model Development
22.2.3 DICOM-RT Data Model Development and Data Collection
22.2.4 DICOM-RT Data Conversion and System Integration
22.2.5 Knowledge Base Development
22.2.6 Data Mining for Knowledge and Development of a Quanti.cation and Visualization Tool
22.3 RESULTS OF DEVELOPED QUANTIFIED KNOWLEDGE AND DECISION-SUPPORT TOOLS FOR AN EXAMPLE OF A BRAIN
22.3.1 DICOM-RT ePR Timeline Overview Display
22.3.2 Development of a Visualization Tool with Quanti.ed Knowledge
22.3.3 Development of aWeb-Based GUI for Visualization of Quanti.ed Knowledge
22.4 DISCUSSION
22.5 CONCLUDING REMARKS
References
23. Lossless Digital Signature Embedding Methods for Assuring 2D and 3D Medical Image Integrity Zhen
23.1 INTRODUCTION
23.2 PROCEDURES AND METHODS
23.2.1 General LDSE Method
23.2.2 2D LDSERS Algorithm10,11
23.2.2.1 Algorithm de.nition
23.2.2.2 Embedding
23.2.3 General 3D LDSE Method
23.2.3.1 Signing and embedding
23.2.3.2 Extracting and verifying
23.2.4 3D LDSERS Algorithm
23.2.4.1 Embedding
23.2.4.2 Extracting
23.2.5 From A 3D Volume to 2D Image(s)
23.3 RESULTS
23.3.1 Data Collection
23.3.2 2D LDSERS Results
23.3.3 Time Performance of 2D LDSERS
23.3.4 3D LDSERS Results
23.3.5 Time Performance of 3D LDSERS
23.3.5.1 Sign or verify
23.3.5.2 Embed or extract
23.3.5.3 3D LDSERS vs 2D LDSERS
23.4 APPLICATION OF 2D AND 3D LDSE IN CLINICAL IMAGE DATA FLOW
23.4.1 Application of the LDSE Method in a Large Medical Imaging System Like PACS
23.4.2 Integration of the 3D LDSE Method with the Two IHE Pro.les
23.4.3 Integration of the 3D LDSE Method with Key Image Note
23.4.4 Integration of the 3D LDSE Method with 3D Post-Processing Work.ow
23.5 CONCLUDING REMARKS
APPENDIX I
References
Part III. Medical Imaging Applications, Case Studies and Future Trends
24. The Treatment of Super.cial Tumors Using Intensity Modulated Radiation Therapy and Modulated Ele
24.1 INTRODUCTION
24.2 INTENSITY MODULATED RADIATION THERAPY+ ELECTRONS
24.2.1 Basic Principles of IMRT+ e
24.2.2 Clinical Applications of IMRT+e
24.2.2.1 Cancer of the orbit
24.2.2.2 Cancer of the scalp
24.2.3 Summary
24.3 MODULATED ELECTRON RADIATION THERAPY
24.3.1 Basic Principles of MERT
24.3.1.1 Notations for parameters and constraints
24.3.1.2 Objective function and gradient
24.3.2 Clinical Applications of MERT
24.3.2.1 Cancer of the breast
24.3.2.2 Cancer of the parotid gland
24.4 SUMMARY
24.5 FUTURE TRENDS
24.6 ACKNOWLEDGMENTS
References
25. Image Guidance in Radiation Therapy Maria YY Law
25.1 INTRODUCTION
25.2 IMAGE-GUIDED RADIOTHERAPY
25.3 IMAGE-GUIDED TECHNOLOGIES FOR RADIATION THERAPY
25.3.1 Imaging for Radiation Therapy Planning
25.3.1.1 Multimodality imaging
25.3.1.2 Imaging for organ motion
25.3.2 Imaging for Treatment Delivery
25.3.2.1 MV/KV 2D imaging
25.3.2.2 Room mounted KV .uoroscopic imaging system
25.3.2.3 Integrated CT/linear accelerator
25.3.2.4 Helical MV CT
25.3.2.5 Cone beam CT (CBCT)
25.3.2.6 Ultrasound-guided radiation therapy
25.3.3 Strategies for Error Correction
25.3.4 Frequency of Imaging
25.4 CLINICAL RESULTS
25.5 FUTURE WORK
25.6 SUMMARY
References
26. Functional Brain Mapping and Activation Likelihood Estimation Meta-Analysis Angela R Laird, Jack
26.1 META-ANALYSIS OF THE FUNCTIONAL BRAIN MAPPING LITERATURE
26.2 ACTIVATION LIKELIHOOD ESTIMATION (ALE)
26.2.1 The ALE Statistic
26.2.2 Permutation Tests
26.2.3 Modi.cations to the ALE Approach
26.3 ALE META-ANALYSES OF HUMAN COGNITION AND PERCEPTION
26.3.1 Meta-Analysis of Stroop Interference Studies
26.4 ANALYSIS OF META-ANALYSIS NETWORKS (RDNA AND FSNA)
26.5 CONCLUDING REMARKS
References
27. Dynamic Human Brain Mapping and Analysis: From Statistical Atlases to Patient-Speci.c Diagnosis
27.1 INTRODUCTION: THE CONCEPT OF STATISTICAL ATLASES
27.2 SPATIAL NORMALIZATION AND THE CONSTRUCTION OF A STATISTICAL ATLAS
27.3 STATISTICAL ATLASES OF THE SPATIAL DISTRIBUTION OF BRAIN TISSUE: MEASURING PATTERNS OF BRAIN AT
27.4 MEASURING DYNAMIC PATTERNS OF BRAIN ATROPHY
27.5 FROM THE STATISTICAL ATLAS TO THE INDIVIDUAL DIAGNOSIS
27.6 SUMMARY AND CONCLUSION
27.7 ACKNOWLEDGMENTS
References
28. Diffusion Tensor Imaging Based Analysis of Neurological Disorders Tianming Liu and Stephen TC Wo
28.1 INTRODUCTION
28.2 BACKGROUND AND LITERATURE REVIEW: APPLICATION OF DWI/DTI IN STUDYING NEUROLOGICAL DISORDERS
28.2.1 Aging and Neurodegenerative Diseases
28.2.2 Neurodevelopment and Neurodevelopmental Disorders
28.2.3 Neuropsychiatric Disorders
28.2.4 Neurooncology and Neurosurgical Planning
28.3 PROCEDURES AND METHODS: AUTOMATED WHOLE BRAIN GM DIFFUSIVITY ANALYSIS
28.3.1 Overview of the Computational Framework
28.3.1.1 SPGR space
28.3.1.2 DWI/DTI space
28.3.2 GM Parcellation in SPGR Space
28.3.3 Tissue Classi.cation in DWI/DTI Space
28.3.3.1 Motivation
28.3.3.2 Tissue classi.cation based on ADC and FA images
28.3.4 Multichannel Fusion
28.4 RESULTS AND FINDINGS
28.4.1 GM Diffusivity Study of Normal Brains
28.4.2 GM Diffusivity Study of Creutzfeldt-Jakob Disease
28.5 DISCUSSIONS AND CONCLUDING REMARKS
28.6 ACKNOWLEDGMENTS
References
29. Intelligent Computer Aided Interpretation in Echocardiography: Clinical Needs and Recent Advance
29.1 INTRODUCTION: CARDIAC IMAGING USING ULTRASOUND
29.2 CLINICAL BACKGROUND: A NEED AND AN OPPORTUNITY FOR INTELLIGENT COMPUTER AIDED INTERPRETATION
29.3 CHALLENGES: CAN A COMPUTER DO IT?
29.4 EXISTING SOLUTIONS: FROM SIMPLE THRESHOLDING TO OPTIMIZATION AND POPULATION MODELS
29.4.1 Thresholding and Edge Detection
29.4.2 Energy Minimization and Optimization
29.4.3 Model-Based Methods
29.5 A NEW PARADIGM: LEARNING A DEFORMABLE SEGMENTATION
29.5.1 Learning to Localize the Left Ventricle
29.5.2 Learning Local Deformations
29.5.2.1 A CBIR approach to shape inference
29.5.2.2 Learning a ranking over deformations
29.5.2.3 Learning a regression function from appearance to shape
29.5.3 A Coarse-to-Fine Detection Hierarchy
29.5.4 Motion Analysis: Ejection Fraction, Volume-Time Curve, and Wall Motion
29.6 CONCLUSION
References
30. Current and Future Trends in Radiation Therapy Yulin Song and Guang Li
30.1 INTRODUCTION
30.2 GENERAL PROCESS OF RADIATION THERAPY
30.2.1 Patient Positioning
30.2.2 Patient Immobilization
30.2.3 CT Simulation
30.2.4 Image Registration
30.2.5 Target Delineation
30.2.6 Treatment Planning
30.2.7 Pretreatment Quality Assurance (QA)
30.2.8 Treatment Delivery
30.3 STEREOTACTIC BODY RADIATION THERAPY (SBRT)
30.3.1 Comparison of SBRT with Stereotactic Radiosurgery (SRS)
30.3.2 Hypo-Fractioned, Ablative RT for Extra-Cranial Lesion
30.3.3 Body Immobilization and Respiratory Control
30.3.4 Extremely High Conformal Dose Planning and Delivery
30.4 PROTON AND HEAVY-ION RADIATION THERAPY
30.4.1 Advantage of the Bragg Peak: Sparing Critical Normal Tissue
30.4.2 Advantage of the Radiobiological Ef.cacy: Overcoming Tumor Hypoxia
30.4.3 Cost Disadvantage and Technical Challenges
30.5 FOUR-DIMENSIONAL RADIATION THERAPY (4DRT)
30.5.1 The Concept of 4DRT
30.5.2 Potential Advantage of 4DRT
30.5.3 4D Medical Imaging
30.5.4 4D Treatment Planning
30.5.5 4D Treatment Delivery
30.6 SUMMARY
References
31. IT Architecture and Standards for a Therapy Imaging and Model Management System (TIMMS) Heinz U
31.1 INTRODUCTION
31.2 TIMMS AND ITS INTERFACES
31.3 COMPONENTS OF TIMMS AND FUNCTIONALITIES
31.3.1 Engines and Repositories
31.3.2 Major Functionalities
31.3.2.1 Patient speci.c modelling
31.3.2.2 Adaptive work.ow engines
31.3.2.3 Validation processes
31.4 INCORPORATION OF SURGICAL WORKFLOW
31.5 EXAMPLE OF A TIMMS PROJECT
31.5.1 Active Links Between Surgical Work.ow and TIMMS
31.5.2 Preoperative Assessment
31.5.2.1 Initiation of a new TIMMS project
31.5.2.2 Collection of patient information and images
31.5.2.3 Development of the patient model and treatment plan
31.5.3 Operative Procedure
31.5.3.1 Initiation of operation and patient assessment
31.5.3.2 Planning of electrode placement
31.5.3.3 Placement of .ne needle
31.5.3.4 Placement of radiofrequency electrode and ablation of tumor
31.5.3.5 Assessment of initial ablation of tumor and completion of operation
31.5.4 Post-Operative Care
31.5.4.1 Completion of operation and patient assessment
31.6 MODELLING TOOLS OF TIMMS AND STEPS TOWARDS STANDARDS
31.7 GENERAL MOTIVATION FOR STANDARDS IN SURGERY
31.7.1 Meetings
31.7.1.1 Working group 1: Operational ef.ciency and workflow
31.7.1.2 Working group 2: Systems integration and technical standards
31.7.2 Recommendations
31.8 SURGICAL WORKFLOWS (WF) FOR MEDICAL IMAGING (MI) IN SURGERY
31.8.1 Recording of Work.ows
31.8.2 Dynamics of Work.ows and the Model of the Patient
31.9 CONCLUSION
References
32. Future Trends in Medical and Molecular Imaging Atam P Dhawan, HK Huang and Dae-Shik Kim
32.1 FUTURE TRENDS WITH SYNERGY IN MEDICAL IMAGING APPLICATIONS
32.1.1 Trends in Targeted Imaging and Image Fusion
32.1.2 Image Fusion for Surgical Intervention
32.2 TRENDS IN LARGE-SCALE MEDICAL IMAGE DATA STORAGE AND ANALYSIS
32.2.1 PACS-Based Medical Imaging Informatics
32.2.2 Data and Computing Grids for Image-Based Clinical Trials
32.3 MEDICAL IMAGING TO BRIDGE THE GAP BETWEEN DIAGNOSIS AND TREATMENT
32.3.1 Minimally Invasive Spinal Surgery (MISS)—Background
32.3.2 The MISS Procedure
32.3.3 The Informatics Aspect of MISS
32.4 ACKNOWLEDGMENT
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