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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


PART II
2-D/3-D Ultrasound in Vascular Imaging


CHAPTER 3
3-D US Imaging of the Carotid Arteries
Aaron Fenster, Grace Parraga, Anthony Landry, Bernard Chiu, Michaela Egger,
and J. David Spence
3.1 Introduction
Determining the severity of carotid atherosclerotic stenosis has been an important
step in establishing patient management pathways and identifying patients who can
benefit from carotid endarterectomy versus those who should be treated using life-
style and pharmaceutical interventions. Recently a number of research groups have
developed phenotypes other than carotid stenosis using noninvasive imaging. Mon-
itoring carotid plaque progression/regression and identifying vulnerable or
high-risk plaques that can lead to thrombogenic events using noninvasive imaging
tools now involve multiple disciplines and multiple modalities, including image
processing.
Morphological characterization of carotid plaques has been used for risk strati-
fication and genetic research [1, 2], evaluation of a patient’s response to therapy [3],
evaluation of new risk factors [4, 5], and quantification of the effects of new thera-
pies [6]. Although 1-D measurements, such as the intima-media thickness, have
been important for measuring the progression of carotid end-organ disease, 2-D
measurements of a plaque area have been shown to have significant advantages
because the plaque area progresses more than twice as quickly as does the thickness
[4]. The use of conventional ultrasound in measuring plaque size (cross-sectional
area) in a single longitudinal view has several limitations.
Conventional 2-D US has been used to quantify and correlate plaque morphol-
ogy and composition with the risk of stroke; however, the results have been mixed.
Some investigators have reported high accuracies for identifying features such as
intraplaque hemorrhage [7, 8], but others have found only moderate sensitivity and
specificity [9]. Some of these discrepancies are related to the lack of standardization
and the variability of conventional ultrasound exams. However, it is generally
agreed that several ultrasound techniques must be improved in order to more accu-
rately and precisely quantify and characterize plaques.
Conventional 2-D US has also been useful in the assessment of plaque mor-
phology. However, due to insufficient image contrast and to the variability of con-
67

ventional 2-D US exams, accurate assessments of morphological plaque changes (of
volume and surface irregularity) are difficult. Because it is difficult to relocate
the 2-D image of the plaque, monitoring the changes in the development of the
plaque over long periods of time using conventional 2-D US is problematic. In addi-
tion, the reconstruction of a 3-D impression of the vascular anatomy and the
plaque using multiple 2-D images is time consuming and prone to variability and
inaccuracy [10, 11].
Three-dimensional ultrasoundis a recent development that promises to improve
the visualization and quantification of complex anatomy and pathology to monitor
the progression of atherosclerosis [11]. Noninvasive and reproducible 3-D imaging
techniques that allow for the direct visualization and quantification of plaque devel-
opment are becoming more important in serial monitoring of disease progression
and regression [12–15]. Furthermore, the characterization of carotid plaques in
three dimensions will potentially improve investigations into the changes of surface
morphology, plaque geometry, and plaque distribution. These investigations can
provide important information about the effects of antiatherosclerotic therapies.
3.1.1 Limitations of Conventional Carotid Ultrasound Imaging
In conventional carotid ultrasound examinations, the transducer is manipulated
manually over the arteries by a sonographer or a radiologist. This approach works
well when using ultrasound to diagnosis or detect the presence of carotid disease;
however, quantitative measurements are subject to variability. Obtaining the
required information from a series of 2-D images is largely dependent on the skill
and experience of the operator in performing these tasks. This approach is
suboptimal due to the following limitations:
1. Conventional ultrasound images are two dimensional; hence, the
diagnostician must mentally transform multiple images in order to
reconstruct a 3-D impression of the complex 3-D structure of the vessel and
plaque. This may lead to variable and incorrect quantification, size, and
extent of plaque morphology.
2. Conventional 2-D US images are difficult to reproduce; therefore,
conventional 2-D US is nonoptimal for quantitative prospective studies,
particularly where small changes in the plaque due to therapy are being
followed over the course of time.
3. The patient’s anatomy sometimes restricts the angle of the image, resulting in
the inaccessibility of the optimal image plane necessary for the diagnosis of
carotid disease and the assessment of plaque morphology.
4. Diagnostic and therapeutic decisions, at times, require accurate
measurements of the volumes of the lesion. Several techniques for improving
the management of atherosclerosis are now available; however, current 2-D
US volume measuring techniques, which assume an idealized shape of the
lesion to calculate the volume, use only simple measures of the width in a few
views. This method potentially leads to inaccurate results and operator
variability.
68 3-D US Imaging of the Carotid Arteries

3.2 3-D US Imaging of the Carotid Arteries
Recent research has shown that 3-D US of the carotid arteries not only allows for
the visualization, measurement (volume), and characterization of the carotid plaque
but also provides the capability to monitor plaque progression and regression as
well as to identify vulnerable plaques [13, 16–18]. The ability to monitor the pro-
gression and regression (i.e., the changes in volume and morphology) of carotid
plaques quantitatively provides researchers with important information about the
plaque’s response to therapy (e.g., medication or diet mediated lowering of lipids)
and the plaque’s natural history [15, 18–21].
In the following sections, we review the methods developed and utilized for
acquiring 3-D carotid ultrasound images and the tools required for visualizing and
measuring plaque volume. The tools for monitoring the changes in the plaque’s vol-
ume and the plaque’s surface features will also be discussed. For furtherinformation
about the technical and computational aspects of 3-D US, readers should refer to
Chapter 1 of this book and to the recent review articles and books on the subject
[11, 22–28].
3.2.1 3-D Carotid Ultrasound Scanning Techniques
Because images of the carotid arteries require at least a 4-cm scanning length,
real-time 3-D (four-dimensional) systems cannot be used effectively. Thus, all 3-D
US systems that are currently used to acquire images of the carotid arteries are con-
ventional 1-D US transducers that produce 2-D US images. Different methods are
used to locate the positions and orientations of 2-D images within the volume. Two
3-D US approaches have been successfully used to image the carotid arteries:
mechanical linear scanners and magnetically tracked free-hand scanners. These
approaches are summarized in the following sections.
3.2.1.1 Mechanical Linear 3-D Carotid Ultrasound Imaging
Linear scanners use a motorized mechanism to translate the transducer linearly
along the neck of the patient as shown in Figure 3.1. While the transducer is moved,
transverse 2-D images of the carotid arteries are acquired at regular spatial inter-
vals. Each image in the set of acquired 2-D images is spaced equally so that all subse-
quent images are uniform and parallel to the preceding image. The length of the
scan, which is the image length of the carotid arteries in 3-D, depends on the length
of the mechanical scanning mechanism. The resolution of the image in the 3-D scan-
ning direction (i.e., along the artery) can be optimized by varying the translating
speed and sampling interval in order to match the sampling rate to the frame rate of
the ultrasound machine and to match the sampling interval to (half) the elevational
resolution of the transducer [29]. Typically, 2-D US images are acquired every 0.2
mm. If the 2-D US images are acquired at 30 frames per second, a 4-cm length will
require 200 2-D images, which can be scanned in 6.7 seconds without cardiac
gating.
The simple predefined geometry of the acquired 2-D US images can be recon
-
structed to form a 3-D image [29]. Using this approach, a 3-D image can be viewed
3.2 3-D US Imaging of the Carotid Arteries 69

immediately after scanning to determine if additional 3-D scans are necessary. The
specific advantages of immediate review of 3-D images after a scan are that it signifi-
cantly shortens the examination time and reduces digital storage requirements
because a decision can be made not to store unnecessary 3-D images.
Because the 3-D carotid ultrasound image is produced from a series of conven-
tional 2-D images, the resolution in the 3-D image will not be isotropic. In the direc-
tion parallel to the acquired 2-D US image planes, the resolution of the
reconstructed 3-D image will be equal to the original 2-D images; however, in the
direction of the 3-D scan along the arteries, the resolution of the reconstructed 3-D
image will depend on the elevational resolution of the transducer and the interslice
spacing [29]. Because the elevational resolution is less than the in-plane resolution of
the 2-D US images, the resolution of the 3-D US image will be the lowest in the 3-D
scanning direction (i.e., elevation). Therefore, a transducer with good elevational
resolution should be used to obtain optimal results.
The scanning parameters can be adjusted depending on the following two types
of ultrasound images: 3-D B-mode and 3-D power Doppler. The 3-D B-mode is used
for imaging vessel walls and plaque; one to three focal zones result in a 2-D image
acquisition rate of about 30 to 15 frames per second and a 3-D scan of 6.7 to 13.4
seconds for the acquisition of 200 2-D US images. For 3-D power Doppler imaging,
which is used to display blood flow, an increased persistence with no cardiac gating
results in an acquisition rate of 7.5 images per second and a 3-D scan time of 20
seconds.
The 3-D mechanical scanning offers three advantages: short imaging times,
high-quality 3-D images, and fast reconstruction times. However, its bulkiness and
weight sometimes make it inconvenient to use. Linear scanning has been successfully
implemented in many vascular imaging applications using B-mode and color Dopp-
ler images of the carotid arteries [30–34], vascular test phantoms [35–37], and
power Doppler images [30–36]. An example of a mechanical scanner is shown in
Figure 3.1, and several examples of linearly scanned 3-D US images of carotid arter
-
ies with complex plaques are shown in Figure 3.2.
70 3-D US Imaging of the Carotid Arteries
Figure 3.1 Photograph of a mechanical linear scanner mechanism used to acquire 3-D US
images of the carotid arteries. The transducer is translated along the arteries, while conventional
2-D US images are acquired by a computer and reconstructed into a 3-D image.

3.2.1.2 Magnetically Tracked Free-Hand 3-D Carotid Ultrasound Imaging
To overcome size and weight issues related to mechanical scanning, free-hand scan-
ning techniques have been developed that do not require motorized mechanisms
[37–42]. To measure position and orientation, a sensor is attached to the trans-
ducer. As the transducer is manipulated across the neck, a set of transverse 2-D
images of the carotid arteries is digitally acquired by a computer, and the position
and orientation of each image is obtained and recorded. This information is then
used to reconstruct the 3-D image. Because the relative positions of the acquired
2-D images are not predefined, the operator must ensure that the set of 2-D images
does not have any significant gaps and that the desired region is scanned. In addi-
tion, the ability to scan the neck freely allows longer regions of the neck to be
imaged.
Although several free-hand scanning approaches have been developed (e.g.,
articulated arms, acoustic sensing, magnetic field sensing, and image-based sens-
ing), the most successful approach for imaging the carotid arteries uses a sensor
with six degrees of freedom to measure the strength of a magnetic field. In this
approach, a transmitter is used to producea spatially varying magnetic field; a small
sensor detects the magnetic field in order to determine the relative position and ori
-
3.2 3-D US Imaging of the Carotid Arteries 71
(c) (d)
(a) (b)
Figure 3.2 Two 3-D US views of two different patients with complex and ulcerated carotid
plaques. For each patient, (a, c) a transverse and (b, d) a longitudinal view are shown side by side.

entation of the sensor. Typically, the transmitter is placed beside the patient and the
sensor is mounted onto the transducer.
The magnetic field sensors are small and unobtrusive devices that allow the
transducer to be tracked with fewer constraints than any other mechanical scanning
approach. However, electromagnetic interference (e.g., ac power cabling and
motors) can compromise the accuracy of tracking. Geometric distortions in the final
3-D US image can occur if ferrous or highly conductive metals are located nearby.
By ensuring that the environment near the patient is free of metals and electrical
interference, high-quality 3-D images of the carotid arteries can be produced. Figure
3.3 shows a 3-Dcarotid image that has been obtained witha magnetic field sensor.
3.2.2 Reconstruction of 3-D US Images
In the 3-D reconstruction procedure, the set of acquired 2-D images is used to recon-
struct a 3-D (voxel-based) volume. Each of the 2-D US images is placed in its correct
location within the volume. The gray-scale values of any volume element (or voxel)
not sampled by the 2-D images are then calculated by interpolating between the
appropriate images. As a result, all 2-D image information is preserved, allowing for
the viewing of the original 2-D planes as well as the other types of views. The advent
of desktop computers has made this process very efficient by allowing 3-D recon-
structions to occur while 2-D images are being acquired. Thus, it is possible to view
72 3-D US Imaging of the Carotid Arteries
Figure 3.3 A 3-D B-mode ultrasound image of carotid arteries obtained with the free-hand scan
-
ning approach using a magnetic position and orientation measurement. The 3-D image has been
“sliced” to reveal a plaque at the entrance of the internal carotid artery. The plaque is
heterogenous, with calcified regions casting a shadow.

the complete 3-D US image immediately after the acquisition of the images has been
completed.
3.2.3 Viewing of 3-D US Images
After a 3-D image has been reconstructed, it is ready to be viewed. The cube-view
approach is based on multiplanar rendering using texture mapping. In this tech-
nique, a 3-D image is displayed as a polyhedron, and the appropriate image for each
plane is “painted” on the face of the cube (texture mapped). Users not only can
rotate the polyhedron in order to obtain the desired orientation of the 3-D image,
but also can move any of the surfaces (i.e., by slicing of the 3-D image parallel or
obliquely) to the original, while the appropriate data are texture mapped in real
time onto the new face. As a result, users always have 3-D image-based cues, which
relate the plane being manipulated to the rest of the anatomy. These visual cues
allow users toefficiently identify the desired structures [11, 22, 26, 27]. Examples of
this approach are shown in Figures 3.2 and 3.3.
In the orthogonal plane view, three perpendicular planes are extracted from the
volume data and displayed simultaneously on a computer screen as 2-D images.
Graphical cues are added to the screen and overlaid onto the 2-D images to indicate
their relative orientations. Users can then select and move single or multiple planes
within the 3-D image volume to provide a cross-sectional view at any location or
orientation, including oblique views.
The volume rendering technique presents a display of the entire 3-D image after
it has been projected onto a 2-D plane. The projection of the image is typically
accomplished via ray-casting techniques [43]. The image voxels that intersect each
ray are weighted and summed. The desired result is displayed in the rendered image.
Although this approach has been used primarily to display fetal anatomy, it can also
be used to display vascular anatomy [30] and views of carotid plaques, as shown in
Figure 3.4.
3.3 Measurement of Plaque Volume
3.3.1 Plaque Segmentation by Manual Planimetry
Carotid disease can be measured using either of two manual planimetry techniques:
the plaque volume technique or the vessel wall volume technique. When using the
plaque volume technique, each 3-D image is “sliced” transverse to the vessel axis,
starting from one end of the plaque using an interslice difference (ISD) of 1.0 mm
[Figure 3.5(a)]. Using software developed in our laboratory, we can contour the
plaque in each cross-sectional image using a cross-haired cursor (Figure 3.5). As the
contours are manually outlined, the visualization software calculates the area of the
contours automatically. Sequential areas of the contours are averaged and multi-
plied by the ISD in order to calculate the incremental volume. A summation of
incremental volumes provides a measure of the total plaque volume. After measur-
ing a complete plaque volume, the 3-D US image can be viewed in multiple orienta-
tions in order to verify that the set of contours has outlined the entire plaque
3.3 Measurement of Plaque Volume 73
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