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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5835_Библиотеки_им_академика_М_И_Перельмана.pdf
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- •Contents
- •Preface
- •1.1 Introduction
- •1.5 Free-Hand Scanning with Position Sensing
- •1.5.1 Tracked 3-D US with Articulated Arm Sensing
- •1.5.2 Free-Hand 3-D US with Acoustic Tracking
- •1.5.3 Free-Hand 3-D Scanning with Magnetic Field Sensing
- •1.5.4 3-D US Tracked by Speckle Decorrelation
- •1.6 Free-Hand 3-D US Scanning Without Position Sensing
- •1.7 2-D Array Scanning for Dynamic 3-D Ultrasound (4-D US)
- •1.8 3-D Ultrasound Visualization
- •1.8.1 Multiplanar Reformatting
- •1.8.2 Volume Rendering Techniques
- •1.9 3-D Ultrasound-Guided Prostate Therapy
- •1.2 Disadvantages of 2-D Ultrasound
- •1.3 3-D Ultrasound Scanning Techniques
- •1.4 Mechanical Scanning
- •1.4.1 Linear Mechanical 3-D Scanners
- •1.4.2 Tilt 3-D Mechanical Scanners
- •1.4.3 Endocavity Rotational 3-D Scanners
- •1.9.1 Early-Stage Prostate Cancer Management
- •1.9.2 US-Guided Prostate Brachytherapy
- •1.9.3 3-D TRUS-Guided Brachytherapy System
- •1.9.4 3-D TRUS Imaging System
- •1.9.5 Prostate Segmentation
- •1.9.5.1 Initialization
- •1.9.5.2 Refinement
- •1.9.5.3 Propagation
- •1.9.6 Dosimetry
- •1.9.7 Calibration of the Coordinate Systems
- •1.9.8 Needle Segmentation
- •1.10 Evaluation of 3-D TRUS-Guided Brachytherapy System
- •1.10.1 Methods of Calibration
- •1.10.1.1 Fiducial Localization Error
- •1.10.1.2 Fiducial Registration Error
- •1.10.1.3 Target Registration Error
- •1.10.2 Results of Calibration
- •1.10.3 Accuracy of Needle Placement
- •1.10.4 Accuracy of Needle Angulation
- •1.10.5 Accuracy of Needle Targeting
- •1.10.6 Accuracy of Needle Tracking
- •1.10.7 Accuracy of Seed Implantation
- •1.11 Conclusions
- •Acknowledgments
- •References
- •2.1 Introduction
- •2.2 Despeckle Filtering
- •2.2.1 Local Statistics Filtering
- •2.2.1.1 First-Order Statistics Filtering (lsmv, wiener)
- •2.2.1.2 Homogeneous Mask Area Filtering (lsminsc)
- •2.2.2 Median Filtering (median)
- •2.2.3 Maximum Homogeneity over a Pixel Neighborhood Filtering (homog)
- •2.2.4 Geometric Filtering (gf4d)
- •2.2.5 Homomorphic Filtering (homo)
- •2.2.6 Diffusion Filtering
- •2.2.7 Wavelet Filtering (waveltc)
- •2.3 Methodology
- •2.3.1 Material
- •2.3.2 Recording of Ultrasound Images
- •2.3.3 Despeckle Filtering
- •2.3.4 Texture Analysis
- •2.3.4.1 Statistical Features (SF)
- •2.3.4.2 Spatial Gray-Level Dependence Matrices (SGLDMs)
- •2.3.4.3 Gray Level Difference Statistics (GLDS) [44]
- •2.3.4.4 Neighborhood Gray Tone Difference Matrix (NGTDM) [45]
- •2.3.4.5 Statistical Feature Matrix (SFM) [46]
- •2.3.4.6 Laws Texture Energy Measures (TEM) [46]
- •2.3.4.7 Fractal Dimension Texture Analysis (FDTA) [46]
- •2.3.4.8 Fourier Power Spectrum (FPS) [46]
- •2.3.5 Distance Measures
- •2.3.6 Univariate Statistical Analysis
- •2.3.7 kNN Classifier
- •2.3.8 Image Quality Evaluation Metrics
- •2.3.9 Visual Evaluation by Experts
- •2.4 Results
- •2.4.1 Evaluation of Despeckle Filtering on a Symptomatic Ultrasound Image
- •2.4.2 Texture Analysis: Distance Measures
- •2.4.3 Texture Analysis: Univariate Statistical Analysis
- •2.4.4 Texture Analysis: kNN Classifier
- •2.4.5 Image Quality Evaluation Metrics
- •2.4.6 Visual Evaluation by Experts
- •2.5 Discussion
- •Acknowledgments
- •References
- •3.1 Introduction
- •3.1.1 Limitations of Conventional Carotid Ultrasound Imaging
- •3.2 3-D US Imaging of the Carotid Arteries
- •3.2.1 3-D Carotid Ultrasound Scanning Techniques
- •3.2.1.1 Mechanical Linear 3-D Carotid Ultrasound Imaging
- •3.2.2 Reconstruction of 3-D US Images
- •3.2.3 Viewing of 3-D US Images
- •3.3 Measurement of Plaque Volume
- •3.3.1 Plaque Segmentation by Manual Planimetry
- •3.3.8 Local Distribution and Change of Carotid Wall and Plaque
- •3.3.8.1 Reconstructing Surfaces from Polygonal Slices
- •3.3.8.2 3-D Maps of Average and Standard Deviation Carotid Surfaces
- •3.3.2 Studies of Plaque Volume
- •3.3.3 The Effects of Interslice Distance
- •3.3.4 The Accuracy of Plaque Volume Measurements
- •3.3.5 Variability of Plaque Volume Measurements
- •3.3.6 Automated Segmentation of Plaques from 3-D US Images
- •3.3.7 Plaque Surface Morphology
- •3.3.8.3 Vessel Wall Plus Plaque Thickness Map and Its Standard Deviation
- •3.3.8.4 3-D Maps of Carotid Wall Plus Plaque Thickness Change
- •3.3.9 Example Results
- •3.4 Trends and Future Developments
- •3.4.1 The Need for Improved Free-Hand Scanning
- •3.4.2 The Need for Improved Visualization Tools
- •3.4.3 Reduction in the Variability of Plaque Volume Measurement
- •3.4.4 ECG-Triggered Acquisition of 3-D Carotid US Images
- •Acknowledgments
- •References
- •4.1 Introduction
- •4.2 Methodology
- •4.2.1 Ultrasound Imaging Scanners
- •4.2.2 Material
- •4.2.3 Speckle Reduction
- •4.2.4 Image Normalization
- •4.2.5 Statistical and Texture Analysis
- •4.2.5.2 Spatial Gray-Level Dependence Matrix—Range of Values (SGLDM)
- •4.3 Image Quality and Evaluation Metrics
- •4.3.1 Normalized Mean-Square-Error
- •4.3.2 Normalized Root Mean-Square-Error
- •4.3.3 Normalized Error Summation in the Form of the Minkowski Metric
- •4.3.4 Normalized Geometric Average Error
- •4.3.5 Normalized Signal-to-Noise Ratio
- •4.3.6 Normalized Peak Signal-to-Noise Radio
- •4.3.7 Mathematically Defined Universal Quality Index
- •4.3.8 Structural Similarity Index
- •4.4 Visual Perception Evaluation
- •4.5 Results
- •4.6 Discussion
- •Acknowledgments
- •References
- •5.1 Ultrasound Imaging of the Carotid Wall
- •5.1.1 Carotid Artery Anatomy and Pathology
- •5.1.2 Principles and Validity of Ultrasound Analysis of the Carotid Artery
- •5.1.2.1 Normal Carotid Wall
- •5.1.2.2 Ultrasound Analysis of Plaques
- •5.2.1 Different Approaches for ROI Extraction
- •5.2.2 Approaches to Image Segmentation
- •5.3 Structure of the CULEX Algorithm
- •5.3.1 ROI Extraction
- •5.3.2 Segmentation of the Lumen and Carotid Wall
- •5.3.3 Notes About the CULEX Segmentation Strategy
- •5.4 CULEX Characterization and Image Databases
- •5.4.1 Percent Statistics Algorithm
- •5.4.2 Confidence Interval for the Average Segmentation
- •5.4.3 Segmentation Error
- •5.4.4 Image Database and Ultrasound Equipment
- •5.5 ROI Detection and Robustness to Noise
- •5.6.1 Absolute Segmentation Errors and IMT Measurements
- •5.6.2 Percent Statistics
- •5.6.3 Confidence Interval for the Mean Segmentation
- •5.7 Plaque Imaging
- •5.7.1 Stable Plaques
- •5.7.2 Unstable Plaques (Echolucent Type II)
- •5.8 Discussion and Conclusions
- •References
- •6.1 Introduction
- •6.2 Level Set Segmentation Method
- •6.2.1 Basic Level Set Theory
- •6.2.2 Summary of Level Set Theory
- •6.3 Proposed Architecture for the CAD System
- •6.3.1 Data Acquisition
- •6.3.2 Preprocessing Subsystem Architecture
- •6.3.2.1 Anisotropic Diffusion Filtering
- •6.3.2.2 Image Enhancement Via Sigmoid Mapping
- •6.3.3 Segmentation Subsystem Based on the Level Set Framework
- •6.3.4 Beam Angle Statistics
- •6.3.5 Similarity Between Shapes: A Classification Strategy
- •6.3.5.1 Best-Fit Ellipse Computation
- •6.3.5.2 Similarity Measurement Between a True Lesion and a Fitted Lesion
- •6.4 Experimental Protocol, Data Analysis, and Performance Evaluation
- •6.4.1 Similarity Measure Analysis
- •6.4.2 Performance Evaluation
- •6.5 Discussion
- •6.5.1 Discussion About the Dual Level Set
- •6.5.2 Discussion About the Similarity Measure
- •6.5.3 Discussion About Threshold Selection
- •6.6 Conclusion and Future Works
- •References
- •7.1 Introduction
- •7.2 Review of the 2-D Continuous Strain Imaging System
- •7.3 Proposed Architecture of the 3-D Strain Imaging System
- •7.3.1 Data Acquisition
- •7.3.2 Image Enhancement Via Sigmoid Mapping
- •7.3.3 Segmentation Subsystem Based on the Level Set Framework
- •7.3.3.1 Basic Theory of the Level Set
- •7.3.3.2 3-D Strain Imaging and Segmentation
- •7.3.4.1 Normalized Volume Difference
- •7.3.4.2 Normalized Shape Difference
- •7.3.4.3 Normalized Contour Difference
- •7.3.4.4 Shift Distance
- •7.4 Experimental Protocol, Data Analysis, and Results
- •7.4.1 2-D and 3-D Experimental Protocols
- •7.4.2 Segmentation Results
- •7.4.3 Classification Strategy Using ROC Curves and SVM
- •7.4.4 Figure of Merit for 2-D Versus 3-D: Performance Evaluation
- •7.5 Discussions
- •7.5.1 Discussion About the Level Set Framework
- •7.5.2 Discussion About Strain Features
- •7.5.3 Discussion About 3-D Versus 2-D Strain Methods
- •7.6 Conclusion
- •References
- •8.1 Introduction
- •8.2 Related Works
- •8.2.1 Breast Cancer Diagnosis with Autocorrelation Matrix
- •8.2.2 Solid Breast Nodules with Neural Networks
- •8.3 Theoretical Background
- •8.3.1 Tsallis Entropy
- •8.3.2 The Nonextensive Segmentation Approaches
- •8.3.2.1 Nonextensive Segmentation Algorithm for Image Binarization
- •8.3.2.2 The Nonextensive Segmentation Recursive Algorithm
- •8.3.2.3 The Level Set Formulation
- •8.3.2.4 The Support Vector Machine Theory
- •8.3.3 Morphological Chains: Mathematical Morphology Definitions
- •8.4 The Proposed Five-Step Methodology
- •8.5 Performance Evaluation
- •8.6 Discussion and Conclusions
- •Acknowledgments
- •References
- •9.1 Real-Time 3-D Echocardiography
- •9.2 Anisotropic Diffusion
- •9.3.1 Correlation-Based Optical Flow
- •9.3.2 3-D Ultrasound Datasets
- •9.3.3 Surface Tracing
- •9.3.4 Surface Tracking with Optical Flow
- •9.3.5 Evaluation
- •9.3.6 Results
- •9.3.6.1 Dog Data
- •9.3.6.2 Clinical Data
- •9.4 Dynamic Cardiac Information from Optical Flow
- •9.4.1 Coordinate Systems
- •9.4.2 Dynamic Cardiac Information Measurements
- •9.4.3 Data
- •9.4.4 Results and Discussion
- •9.4.5 Discussion About Estimation of the Myocardial Field
- •9.5 Summary
- •Acknowledgments
- •References
- •10.1 Introduction
- •10.1.1 Left Ventricular Systolic Dysfunction
- •10.1.2 Determinants of LV Systolic Performance
- •10.2 Assessment of Left Ventricular Function—An Overview
- •10.2.1 Ejection Phase Indices
- •10.2.1.1 Ejection Fraction and Fractional Shortening
- •10.2.1.2 Myocardial Performance Index
- •10.2.2 Isovolumetric Indices
- •10.2.3 Pressure-Volume Analysis
- •10.2.3.1 End Systolic Pressure-Volume Relationships (ESPVR)
- •10.2.3.2 Stroke Work
- •10.2.3.3 Preload Recruitable Stroke Work
- •10.2.4 Left Ventricular Stress Strain Relationship
- •10.3 Echocardiographic Examinations
- •10.3.1 Echocardiographic Measurements and Calculations
- •10.3.1.1 Echocardiographic Measurement of Left Ventricular Volume
- •10.3.1.2 Left Ventricular dp/dt
- •10.3.1.3 Left Ventricular End-Systolic Wall Stress
- •10.3.1.4 Left Ventricular Forward Stroke Volume and Cardiac Output
- •10.3.1.5 Mitral Regurgitant Volume
- •10.3.1.6 Wall Motion Index
- •10.3.1.7 Mitral Annulus Motion
- •10.3.2 Real-Time Left Ventricular Volume by Automated Boundary Detection
- •10.4 Left Ventricle Systolic Dysfunction Identification
- •10.4.1 Delineation of Left Ventricle
- •10.4.2 Quantification of Systolic Dysfunction
- •10.4.3 Region of Dysfunction Identification
- •10.4.3.1 Centerline Method
- •10.4.3.2 Color Kinesis Method
- •10.4.3.3 Anatomic M-Mode
- •10.4.3.4 Radial Line Method
- •10.4.3.5 Regional Area Method
- •10.5 Real-Time 3-D Echocardiography
- •10.5.1 Sparse Array Transducer
- •10.5.2 Full Matrix Array
- •10.5.3 Real-Time Volume Rendered 3-D Imaging
- •References
- •11.1 Introduction
- •11.2 Origin of Heart Sounds
- •11.2.1 Auscultatory Areas
- •11.2.2 Rate and Rhythm
- •11.2.3 Normal Heart Sounds
- •11.2.3.1 First Heart Sound (S1)
- •11.2.3.2 Second Heart Sound (S2)
- •11.2.3.3 Third Heart Sound (S3)
- •11.2.3.4 Fourth Heart Sound (S4)
- •11.2.4 Abnormal Heart Sounds
- •11.2.4.1 Abnormal S1
- •11.2.4.2 Abnormal S2
- •11.2.5 Murmur Classifications
- •11.2.5.1 Murmur Mechanisms
- •11.2.7 Gallop Sounds
- •11.2.8 Clicks
- •11.2.9 Opening Snaps
- •11.2.9.1 Tricuspid Valve Opening Snap
- •11.2.9.2 Mitral Valve Opening Snap
- •11.2.9.3 Differential Diagnosis of MVOS Versus S3 Gallop
- •11.2.9.4 Differential Diagnosis of MVOS Versus Splitting of S2
- •11.2.10 Maneuvers
- •11.3 Phonocardiograph
- •11.3.2 Amplifier
- •11.3.1 Instrument Description
- •11.3.1.1 Transducer
- •11.3.3 Filter
- •11.3.4 Analysis
- •11.3.5 Segmentation of S1 and S2
- •11.3.5.1 Blind Source Separation
- •11.3.5.2 Complexity-Based Gating
- •11.3.6 Decomposition of PCG
- •11.3.7 Short-Term Fourier Transform
- •11.3.8 Continuous Wavelet Transform
- •11.3.8.1 Resolution
- •11.3.9 Wigner Distribution
- •11.3.10 The Choi-Williams Distribution
- •11.3.11 Autoregressive Modeling
- •11.3.12 Feature Extraction
- •11.3.13 Classification
- •11.3.13.1 Neural Networks
- •11.3.13.2 Fuzzy Systems
- •11.3.13.3 Wavelet Method
- •References
- •12.1 Level Set Method
- •12.1.1 Front Evolution
- •12.1.2 Boundary Value Formulation
- •12.1.3 Initial Value Formulation
- •12.1.4 Applications of Level Set Method
- •12.2 Active Contours in Level Set Framework
- •12.2.1 Front Evolving Geometric Models of Active Contours
- •12.2.2 Geodesic Active Contour Models
- •12.2.3 Tuning Geometric Active Contour with Regularizers
- •12.2.3.1 Clustering-Based Regularizers
- •12.2.3.2 Bayesian-Based Regularizers
- •12.2.3.3 Shape-Based Regularizers
- •12.2.3.4 Coupling-Surfaces Regularizers
- •12.2.4 Region-Based Active Contour Models
- •12.2.4.1 Mumford-Shah (MS) Function
- •12.2.4.2 The Level Set Approach to Solving the Mumford-Shah Function
- •12.3 Fully Automated 3-D Prostate Segmentation from Transrectal Ultrasound (TRUS) Images
- •12.3.1 3-D Shape Prior Extraction
- •12.3.2 Intensity Prior Extraction
- •12.3.3 Segmentation in Bayesian Interface
- •12.4 Evaluation of the System Performance
- •12.4.1 Transrectal Ultrasound Images
- •12.4.2 Validation Method
- •12.5 Results
- •12.6 Conclusions and Future Work
- •References
- •13.1 Introduction
- •13.2 Recent Developments in HIFU and Therapy Planning
- •13.2.1 HIFU Applications
- •13.2.2 HIFU Therapy Guidance
- •13.2.3 HIFU Transducers and Instrumentation
- •13.2.4 Theoretical and Experimental Study of the HIFU Mechanism
- •13.2.5 HIFU Dosimetry and Therapy Planning
- •13.3 Generalized Strategies for HIFU Therapy Planning
- •13.3.1 Overview of Generalized Strategy
- •13.3.2 Pretreatment Imaging
- •13.3.3 Defining the Target and Surrounding Tissues
- •13.3.4 Simulation of HIFU
- •13.3.5 Integration and Visualization
- •13.3.6 Planning of HIFU Delivery and Control
- •13.3.7 Overall Software Design and Integration of Tools
- •13.3.8 Validation and Clinical Use of the HIFU Therapy Planning System
- •13.4 Challenges and Work Ahead
- •Acknowledgments
- •References
- •14.1 Introduction to HIFU
- •14.2 Ultrasonic System and Various Measurement Systems
- •14.2.1 Attenuation Measurement
- •14.2.2 Ultrasonic System
- •14.2.3 Acoustical Monitoring of Cavitation
- •14.2.4 Software
- •14.2.5 Acoustical Field Measurement
- •14.2.6 In Vitro Experiments
- •14.2.7 In Vivo Experiments
- •14.3 Simulation Model
- •14.3.1 Estimation of Temperature
- •14.3.2 Estimation of Thermal Dose
- •14.3.3 Estimation of Lesion Size
- •14.3.4 Effect of Attenuation
- •14.4 Attenuation Measurement as a Function of Thermal Dose
- •14.4.1 Results of Attenuation Versus Thermal Dose
- •14.4.2 Simulation Results Using Variable Attenuation
- •14.5 Kidney Ablation
- •14.5.1 Results
- •14.6 Kidney Ablation Under MRI Guidance
- •14.6.1 HIFU Parameters
- •14.6.2 MRI Parameters
- •14.6.3 Results
- •14.7 Liver Ablation
- •14.7.1 HIFU Parameters
- •14.7.2 MRI Analysis
- •14.7.3 Results
- •14.8 Effect of Interfaces in HIFU Ablation
- •14.8.1 MRI Analysis
- •14.8.2 Results
- •References
- •15.1 Introduction
- •15.2 Thermal Ablation Mechanisms
- •15.2.1 Hyperthermia and Ablation
- •15.2.2 Cryoablation
- •15.2.3 Chemical Ablation
- •15.3 Therapeutic Modalities for Thermal Ablation
- •15.3.1 RF Ablation
- •15.3.2 Microwave Ablation
- •15.3.3 Laser Thermal Ablation
- •15.3.4 Focused Ultrasound-Based Ablation
- •15.4 Focal Ultrasound Surgery Robots—FUSBOTs
- •15.5 Concluding Remarks
- •References
- •About the Editors
- •List of Contributors
- •Index

8.4 The Proposed Five-Step Methodology 212
8.5 Performance Evaluation 217
8.6 Discussion and Conclusions 218
Acknowledgments 220
References 220
PART IV
3-D Cardiac Ultrasound Imaging 225
CHAPTER 9
Cardiac Motion Analysis Based on Optical Flow of Real-Time 3-D
Ultrasound Data 227
9.1 Real-Time 3-D Echocardiography 228
9.2 Anisotropic Diffusion 228
9.3 Tracking of LV Endocardial Surface on Real-Time 3-D Ultrasound
with Optical Flow 230
9.3.1 Correlation-Based Optical Flow 231
9.3.2 3-D Ultrasound Datasets 232
9.3.3 Surface Tracing 232
9.3.4 Surface Tracking with Optical Flow 233
9.3.5 Evaluation 233
9.3.6 Results 234
9.4 Dynamic Cardiac Information from Optical Flow 237
9.4.1 Coordinate Systems 238
9.4.2 Dynamic Cardiac Information Measurements 239
9.4.3 Data 240
9.4.4 Results and Discussion 240
9.4.5 Discussion About Estimation of the Myocardial Field 242
9.5 Summary 243
Acknowledgments 243
References 243
CHAPTER 10
Echocardiography: A Tool for LVSD Identification 247
10.1 Introduction 247
10.1.1 Left Ventricular Systolic Dysfunction 248
10.1.2 Determinants of LV Systolic Performance 249
10.2 Assessment of Left Ventricular Function—An Overview 249
10.2.1 Ejection Phase Indices 250
10.2.2 Isovolumetric Indices 251
10.2.3 Pressure-Volume Analysis 252
10.2.4 Left Ventricular Stress Strain Relationship 256
10.3 Echocardiographic Examinations 257
10.3.1 Echocardiographic Measurements and Calculations 258
10.3.2 Real-Time Left Ventricular Volume by Automated Boundary
Detection 262
10.4 Left Ventricle Systolic Dysfunction Identification 263
x Contents

10.4.1 Delineation of Left Ventricle 263
10.4.2 Quantification of Systolic Dysfunction 265
10.4.3 Region of Dysfunction Identification 266
10.5 Real-Time 3-D Echocardiography 272
10.5.1 Sparse Array Transducer 273
10.5.2 Full Matrix Array 273
10.5.3 Real-Time Volume Rendered 3-D Imaging 273
References 277
CHAPTER 11
Diagnosis of Heart by Phonocardiography 283
11.1 Introduction 283
11.2 Origin of Heart Sounds 284
11.2.1 Auscultatory Areas 285
11.2.2 Rate and Rhythm 286
11.2.3 Normal Heart Sounds 287
11.2.4 Abnormal Heart Sounds 288
11.2.5 Murmur Classifications 289
11.2.7 Gallop Sounds 291
11.2.8 Clicks 292
11.2.9 Opening Snaps 293
11.2.10 Maneuvers 294
11.3 Phonocardiograph 294
11.3.1 Instrument Description 296
11.3.2 Amplifier 297
11.3.3 Filter 297
11.3.4 Analysis 297
11.3.5 Segmentation of S1 and S2 298
11.3.6 Decomposition of PCG 300
11.3.7 Short-Term Fourier Transform 301
11.3.8 Continuous Wavelet Transform 302
11.3.9 Wigner Distribution 303
11.3.10 The Choi-Williams Distribution 303
11.3.11 Autoregressive Modeling 305
11.3.12 Feature Extraction 306
11.3.13 Classification 307
References 309
PART V
3-D Prostate Ultrasound Imaging 311
CHAPTER 12
Prostate Analysis Using Ultrasound Imaging 313
12.1 Level Set Method 315
12.1.1 Front Evolution 315
12.1.2 Boundary Value Formulation 316
12.1.3 Initial Value Formulation 316
Contents xi

12.1.4 Applications of Level Set Method 317
12.2 Active Contours in Level Set Framework 318
12.2.1 Front Evolving Geometric Models of Active Contours 319
12.2.2 Geodesic Active Contour Models 319
12.2.3 Tuning Geometric Active Contour with Regularizers 319
12.2.4 Region-Based Active Contour Models 321
12.3 Fully Automated 3-D Prostate Segmentation from Transrectal
Ultrasound (TRUS) Images 325
12.3.1 3-D Shape Prior Extraction 327
12.3.2 Intensity Prior Extraction 333
12.3.3 Segmentation in Bayesian Interface 334
12.4 Evaluation of the System Performance 338
12.4.1 Transrectal Ultrasound Images 338
12.4.2 Validation Method 338
12.5 Results 339
12.6 Conclusions and Future Work 340
References 342
PART VI
Application of 3-D Therapeutic and Image Guidance Ultrasound 347
CHAPTER 13
HIFU Therapy Planning Using Pretreatment Imaging and Simulation 349
13.1 Introduction 349
13.2 Recent Developments in HIFU and Therapy Planning 351
13.2.1 HIFU Applications 351
13.2.2 HIFU Therapy Guidance 351
13.2.3 HIFU Transducers and Instrumentation 352
13.2.4 Theoretical and Experimental Study of the HIFU Mechanism 352
13.2.5 HIFU Dosimetry and Therapy Planning 353
13.3 Generalized Strategies for HIFU Therapy Planning 354
13.3.1 Overview of Generalized Strategy 355
13.3.2 Pretreatment Imaging 355
13.3.3 Defining the Target and Surrounding Tissues 356
13.3.4 Simulation of HIFU 357
13.3.5 Integration and Visualization 358
13.3.6 Planning of HIFU Delivery and Control 359
13.3.7 Overall Software Design and Integration of Tools 359
13.3.8 Validation and Clinical Use of the HIFU Therapy Planning
System 362
13.4 Challenges and Work Ahead 362
Acknowledgments 363
References 363
CHAPTER 14
MR-Guided HIFU for Kidney and Liver Applications 367
14.1 Introduction to HIFU 367
xii Contents

14.2 Ultrasonic System and Various Measurement Systems 368
14.2.1 Attenuation Measurement 368
14.2.2 Ultrasonic System 369
14.2.3 Acoustical Monitoring of Cavitation 369
14.2.4 Software 370
14.2.5 Acoustical Field Measurement 370
14.2.6 In Vitro Experiments 371
14.2.7 In Vivo Experiments 372
14.3 Simulation Model 372
14.3.1 Estimation of Temperature 372
14.3.2 Estimation of Thermal Dose 373
14.3.3 Estimation of Lesion Size 373
14.3.4 Effect of Attenuation 373
14.4 Attenuation Measurement as a Function of Thermal Dose 374
14.4.1 Results of Attenuation Versus Thermal Dose 375
14.4.2 Simulation Results Using Variable Attenuation 376
14.5 Kidney Ablation 376
14.5.1 Results 376
14.6 Kidney Ablation Under MRI Guidance 380
14.6.1 HIFU Parameters 381
14.6.2 MRI Parameters 381
14.6.3 Results 382
14.7 Liver Ablation 386
14.7.1 HIFU Parameters 386
14.7.2 MRI Analysis 386
14.7.3 Results 387
14.8 Effect of Interfaces in HIFU Ablation 390
14.8.1 MRI Analysis 392
14.8.2 Results 392
References 396
CHAPTER 15
Modalities and Devices for Thermal Ablation 401
15.1 Introduction 401
15.2 Thermal Ablation Mechanisms 402
15.2.1 Hyperthermia and Ablation 402
15.2.2 Cryoablation 403
15.2.3 Chemical Ablation 403
15.3 Therapeutic Modalities for Thermal Ablation 404
15.3.1 RF Ablation 404
15.3.2 Microwave Ablation 405
15.3.3 Laser Thermal Ablation 406
15.3.4 Focused Ultrasound-Based Ablation 407
15.4 Focal Ultrasound Surgery Robots—FUSBOTs 410
15.5 Concluding Remarks 411
References 412
Contents xiii

About the Editors 415
List of Contributors 417
Index 419
xiv Contents

Preface
The use of ultrasound imaging in medicine has undergone an enormous increase in
the last 10 years, due to strong technological improvements and to the availability
of more accurate and effective strategies for the representation of the ultrasound
informative content. During the last decade, the 3-D visualization technique was
introduced into clinical practice; new contrast agents were developed to improve
the ultrasound diagnosis of heart, liver, brain, and vessel diseases; and highly
focused ultrasound beams were used to perform tissue ablation.
This book covers the most recent advances in clinical and therapeutic ultra-
sound imaging in medicine. It consists of six parts, each dedicated to important clin-
ical applications that represent the state of the art in the fields of ultrasound-based
medical diagnosis or therapy.
Part I covers the recent advances in ultrasound instrumentation. Chapter 1
reviews the development of 3-D ultrasound devices and visualization strategies
adopted in the analysis of complex anatomical structures and in monitoring and
guiding interventional procedures. Chapter 2 deals with the most innovative filter-
ing techniques to cope with speckle noise.
Part II is devoted to vascular applications. Chapter 3 reviews the 3-D applica-
tion to carotid artery analysis. Chapter 4 proposes the quality evaluation of carotid
ultrasound images. Chapter 5 describes the most modern techniques to perform
carotid wall segmentation.
The classification of breast lesions is the topic covered by Part III. Chapter 6
shows the 3-D classification of breast lesions. Chapter 7 deals with strain images
and elasticity measure. Chapter 8 focuses on the breast lesion classification by
means of a support vector machine.
Ultrasound imaging has played a fundamental role in cardiology since the
early development of the first duplex devices. In Part IV, Chapter 9 shows the new
diagnostic possibilities offered by the 4-D ultrasound devices. Chapter 10 illus-
trates innovative contributions in the assessment of systolic dysfunction and heart
failure. Chapter 11 introduces innovative diagnostic methodologies based on
phonocardiography.
In Part V, Chapter 12 provides a thorough description of ultrasound imaging
for prostate cancer detection and shows how recent techniques are capable of creat-
ing extremely accurate 3-D maps.
Part VI is devoted to ultrasound therapy. Chapter 13 illustrates the techniques
to perform treatment planning by ultrasound imaging and simulation. Chapter 14
deals with kidney and liver applications of focused ultrasounds. Chapter 15
describes the recent advances in ultrasound thermal ablation.
xv

On the technical side, this book covers the aspects of 2-D and 3-D image seg-
mentation by deformable geometric and parametric models, of feature analysis and
classification by using intelligent techniques, and of denoising. Registration, realign-
ment, and normalization are specific techniques presented in the chapter relative to
therapy.
This book is the product of a relatively large international team of ultrasound
experts in several fields, ranging from bioengineering to medicine, from clinical
practice to technological development, and from advanced diagnosis to the further
frontiers of therapy.
Jasjit S. Suri
Chirinjeev Kathuria
Ruey-Feng Chang
Filippo Molinari
Aaron Fenster
Editors
June 2008
xvi Preface

PART I
Recent Advances in Ultrasound
Instrumentation


CHAPTER 1
3-D Ultrasound Imaging
Aaron Fenster, Zhouping Wei, and Donal Downey
1.1 Introduction
Since the discovery of x-rays, two-dimensional (2-D) radiographic imaging has been
the most widely used approach for imaging anatomic structures, diagnosing dis-
ease, and guiding interventions. Unfortunately, 2-D x-ray imaging cannot provide
physicians with a complete set of information for imaging the human body. The
introduction of computed tomography (CT) in the early 1970s revolutionized diag-
nostic radiology by providing physicians with images of real three-dimensional
(3-D) anatomic structures, reconstructed from contiguous tomographic 2-D images
of the human body. However, the development of multislice CT imaging,
cone-beam CT imaging, and 3-D magnetic resonance imaging (MRI)has provided a
major impetus to the field of 3-D visualization, which has, in turn, led to the devel-
opment of a wide variety of applications in diagnostic medicine.
3-D visualization techniques of the interior of the human body have increased
the accuracy of diagnoses, the complexity of interventions, and the safety of inter-
ventions to patients. In the past three decades, developments of 3-D CT, MRI, and
computed rotational angiography have accelerated and improved the use of 3-D
visualization in many interventional procedures. Because new minimally invasive
3-D image-guided techniques provide important benefits to patients (i.e., less pain,
less trauma, faster recovery), health care providers (i.e., faster and safer procedures,
better outcomes), taxpayers (i.e., shorter, less expensive hospital stays), employers
(i.e., less absenteeism), and hospitals (i.e., improved outcomes with shorter lengths
of stay), they are rapidly replacing traditional practices.
Although 3-D CT, MRI, and x-ray techniques are important imaging modali-
ties, 3-D ultrasound(US) has been shown toplay a major role in medical diagnostics
and minimally invasive image-guided interventions [1, 2]. Three-dimensional US
imaging has provided high-definition images of complex anatomic structures and
pathology to diagnose disease and to monitor and guide interventional procedures.
Researchers have begun to incorporate 3-D visualizations into ultrasound instru-
mentation as well as integrate 3-D ultrasound into biopsy and therapy procedures
[3–5] for daily use.
This chapter reviews the development of 3-D US and describes our application
of 3-D US for image-guided prostate brachytherapy.
3
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