Frontiers Of Medical Imaging

Description

There has been great progress and increase in demand for medical imaging. The aim of this book is to capture all major developments in all aspects of medical imaging. As such, this book consists of three major parts: medical physics which includes 3D reconstructions, image processing and segmentation in medical imaging, and medical imaging instruments and systems. As the field is very broad and growing exponentially, this book will cover major activities with chapters prepared by leaders in the field. This book takes a balanced approach in providing coverage of all major work done in the field, and thus provides readers a clear view of the frontier activities in the field. Other books may only focus on instrumentation, physics or computer algorithms. In contrast, this book contains all components so that the readers will obtain a full picture of the field. At the same time, readers can gain some deep insights into certain special topics such as 3D reconstruction and image enhancement software systems involving MRI, ultrasound, X-ray and other medical imaging modalities. Front cover Half-title Frontiers of Medical Imaging Copyright Preface Contents Part 1: Theory, Techniques and Physics Chapter 1 Coronary Plaque Quantification by Multi-slice Computed Tomography 1. Introduction 2. MSCT Procedure 3. Coronary Plaque Segmentation 3.1. MSCT-CA Image Processing 3.2. Quantitative MSCT-CA and Validation 3.3. Results of QMSCT-CA Validation 4. MSCT-CA Plaque Composition 4.1. MSCT Derived Tissue Component Information 4.1.1. Literature overview 4.2. Quantitative MSCT-CA Plaque Composition Measurements 5. Discussion 6. Future Developments 7. Conclusions References Chapter 2 On-The-Fly Monte Carlo Methods In Iterative Pet Reconstruction 1. Introduction 2. Error Analysis 2.1. Deterministic Approximation 2.2. Random Approximation 2.2.1. Center of the fluctuation 2.2.2. Amplitude of the fluctuation 2.2.3. Optimal randomization 3. Photon Tracing 4. Statistical Filtering 5. LOR Space Blurring 6. Flat-land Results 7. Application in Fully-3D Reconstruction 8. Conclusions Acknowledgement References Chapter 3 Advances In Online Dynamic MRI Reconstruction 1. Introduction 2. Offline Reconstruction 3. Online Reconstruction 3.1. Compressed Sensing Based Techniques 3.1.1. k-t FOCUSS 3.1.2. Least Squared CS for dynamic MRI reconstruction 3.1.3. Real-time CS based dynamic MRI reconstruction 3.2. Kalman Filter Based Techniques 3.3. Hybrid Methods 4. Conclusion References Chapter 4 Using Prior Information To Enhance Sensitivity of Longitudinal Brain Change Computation 1. Introduction 2. Incorporating Prior Information into Longitudinal Image Registration 2.1. Image Preprocessing 2.2. Mathematical Formulation of TBM with Incorporated Prior Information 2.2.1. Formulation of the image matching problem 2.2.2. Outline of the TBM algorithm 2.2.3. The fluid-flow solution 2.2.4. The Kullback-Liebler penalty 2.2.5. Incorporating prior information into the energy functional 2.2.6. Refining the estimates of edge occurrence 2.2.7. Derivation of the variational derivatives incorporating prior information 3. Experiments and Results 3.1. Change vs. No-Change Images 3.2. Examination of the Enhancements to Edge and Non-Edge Probabilities 3.3. Statistical Power of the Old and New Methods 4. Discussion References Chapter 5 X-Ray Fluorescence Computed Tomography for Molecular Imaging 1. Introduction 2. Materials and Methods 2.1. Phantom Preparation 2.2. Experimental Setup 2.2.1. X-ray source 2.2.2. XFCT acquisition scheme 2.2.3. X-ray fluorescence detector system 2.3. Data Acquisition 2.4. Data Processing 2.4.1. Background subtraction 2.4.2. Image reconstruction 2.4.3. Linearity 2.5. X-ray Dose From XFCT Imaging 3. Results 4. Discussion 5. Conclusions Acknowledgements References Chapter 6 Dictionary Learning Based Low-Dose X-Ray CT Reconstruction 1. Introduction 2. Origin of Idea — From Total-Variation to Dictionary 3. Background — Dictionary Learning based Sparse Representation 4. Dictionary Learning based CT Reconstruction 4.1. Reconstruction Framework 4.1.1. MAP based objective function 4.1.2. GDSIR and ADSIR 4.1.3. Optimization via alternating minimization 4.2. Parameter Selection 4.3. Representative Results — Sheep Lung Study 4.3.1. Data acquisition 4.3.2. Global dictionary learning 4.3.3. Low-dose results 4.3.4. Few-view test 4.3.5. Plots of the terms in the objective function 5. Discussion — TV or Dictionary? Acknowledgements References Chapter 7 Machine Learning Methods for Segmenting Psoriatic Lesions from 2D Images 1. Introduction 2. Application of Machine Learning Methods for Psoriasis Segmentation 2.1. Psoriasis 2.2. Psoriasis Segmentation 3. Segmenting Psoriasis Symptoms: Erythema 3.1. Separating Skin from Background 3.2. Feature Extraction: Decomposing Skin Color into Melanin and Hemoglobin Components 3.3. Erythema Pixel Extraction 4. Segmenting Psoriasis Symptoms: Scaling 4.1. Developing the Feature Space for the Detection of Scaling 4.1.1. Scaling contrast map for enhancing the contrast between scaling and surrounding erythema 4.1.2. Texture analysis with Gabor filters for differentiating rough scaling from normal skin 4.2. Semi-supervised Scaling Segmentation 4.2.1. Removing erythema and other dark pixels 4.2.2. Collecting training samples for segmenting scaling from normal skin 4.2.3. Identifying scaling pixels 5. Experimental Results 5.1. Erythema Segmentation 5.2. Scaling Segmentation 6. Discussions and Conclusion Acknowledgement References Part 2: Image Processing in Medical Imaging Chapter 8 Classification on Brain Functional Magnetic Resonance Imaging: Dimensionality, Sample Size, Subject Variability and Noise 1. Introduction 2. Materials and Methods 3. Results 4. Discussion 5. Concluding Remarks References Chapter 9 Regression Mixture Modeling for fMRI Data Analysis 1. Introduction 2. An Overview of fMRI Data Analysis 3. Finite Mixture of Regression Models 3.1. Mixture Models 3.2. Regression Mixture Modeling 4. Regression Mixture Analysis of fMRI Time-Series 4.1. General Construction 4.1.1. Sparse modeling 4.1.2. Spatial regularization 4.1.3. Multi-kernel scheme 4.2. Estimation of Model Parameters 5. Experiments 5.1. Activation-based fMRI Experiments 5.1.1. Experiments with artificial datasets 5.1.2. Experiments using real fMRI data 5.2. Resting State fMRI Experiments 6. Conclusions References Chapter 10 Tree Structure for Modeling Skin Lesion Growth 1. Introduction 2. Background and Previous Work 2.1. Lesion Growth Model 2.2. Decomposition 3. Decomposing Skin Images into Tree Structures 3.1. Extraction of the Central Point 3.2. Clustering Decomposition 3.3. Tree Construction 4. Skin Lesion Segmentation 5. Skin Lesion Diagnosis 6. Conclusions Acknowledgements References Chapter 11 Automatic Detection of Retinal Structures Based on Mathematical Morphology 1. Introduction 2. Theoretical Background 2.1. Morphological Operators 2.2. Stochastic Watershed Transformation 2.3. Image Enhancement 3. Retinal Vessel Centerline Extraction 4. Optic Disc Segmentation 4.1. Pre-processing 4.2. Processing 4.3. Post-processing 5. Results 5.1. Retinal Vessel Centerline 5.2. Optic Disc 6. Conclusions References Chapter 12 Automatic Segmentation of Retinal Images for Glaucoma Screening 1. Introduction 2. Optic Disc Segmentation 2.1. Background 2.2. Superpixel Generation 2.3. Feature Extraction 2.3.1. Contrast enhanced histogram 2.3.2. Centre surround statistics 2.3.3. Context feature 2.4. Initialization and Deformation 3. Optic Cup Segmentation 3.1. Feature Extraction 3.2. Superpixel Classification for Optic Cup Estimation 3.3. Cup to Disc Ratio 3.4. Confidence Score 4. Experimental Results 4.1. Data Sets 4.2. Agreement Between Automated and Manual Disc, Cup and CDR 4.3. Population-Based Glaucoma Screening Using ARARAT 4.4. Comparison With Other Methods 4.5. Confidence-based Screening 4.6. Inter-observer Errors 5. Discussions and Conclusions Acknowledgements References Chapter 13 Blind Source Separation in Assessing Tumor Pharmacokinetics 1. Introduction 2. Pharmacokinetic Modeling 3. Spatial Independent Component Analysis (ICA) 3.1. Adaptive Complex Independent Component Analysis (AC-ICA) 3.2. Selecting Non-linearity Function for AC-ICA 3.3. Expectation Maximization (EM) 3.4. AC-ICA Implementation and VIF Identification 4. Numerical Phantom 4.1. Contrast Agent (CA) Concentration Modeling 4.2. Generating DCE-MRI Data (Bloch Equations) 4.3. AC-ICA Separation Results (Numerical Phantom) 5. Physical Phantom 5.1. AC-ICA Separation Results for Physical Phantom 6. Prostate Cancer Assessment 6.1. Vascular Input Function (VIF) Calculation in Prostate 6.2. Pharmacokinetic (PK) Analysis Results 7. Conclusions Acknowledgements References Chapter 14 Computer-Aided Tumor Detection in Automated Breast Ultrasound Images 1. Introduction 2. Computer-Aided Tumor Detection Algorithm 2.1. Patients and ABUS Acquisition 2.2. Speckle Noise Reduction 2.3. Multi-Scale Blob Detection 2.4. Tumor Candidate Extraction 2.5. Quantitative Features 2.5.1. Blobness features 2.5.2. Echogenicity features 2.5.3. Morphology features 2.6. Region Classification 2.7. Statistical Evaluation 3. Results 4. Discussions and Conclusion References Part 3: Emerging Methods and Medical Imaging Systems Chapter 15 Histomorphometry of Digital Pathology: Case Study in Prostate Cancer 1. Introduction 2. Hallmarks of Prostate Cancer 3. The Staging of Prostate Cancer 3.1. The Gleason System 3.2. The Clinical Stage 3.3. Pathological Stage 4. Quantitative Image Analysis of Nuclear Morphometry 4.1. Manual Digital Image (Planimetry) Analysis Technology 4.2. Tissue Digital Imaging using Feulgen Stained Nuclei 4.3. The AutoCyte™ Pathology Workstation (AWP) 4.4. Quantitative Nuclear Grade using AutoCyte™ 4.4.1. QNG to study Gleason grade differences 4.4.2. QNG to predict CaP outcomes: biochemical recurrence 4.4.3. QNG to predict CaP metastasis and CaP-specific survival 5. Engineering Approaches to Quantitative Histomorphometry 5.1. Wavelet Transforms 5.2. Intensity Co-occurrence Texture 5.3. Nuclear Shape, Orientation, and Architectural Features for Grading and Assessing Aggressive Prostate Cancers 5.3.1. Adaptive active contour model (AdACM) for nuclei segmentation 5.3.2. Nuclear orientation 5.3.3. Nuclear architecture 5.4. Gland Based Features Grading and Assessing Aggressiveness in Prostate Cancers 5.4.1. Markov random fields for automated gland segmentation 5.4.2. Gland morphology 5.4.3. Gland orientation 6. Summary References Chapter 16 Automated Diagnosis of Diabetic Retinopathy: Fundamentals, Current State of Art and Perspectives 1. Introduction 2. The Retina 3. Diabetic Retinopathy 4. Automated Detection of Diabetic Retinopathy 4.1. Image Quality Assessment 4.2. Segmentation of the Main Anatomical Components of the Retina 4.2.1. Optic disc segmentation 4.2.2. Vascular tree segmentation 4.2.3. Macula segmentation 4.3. Detection of Lesions Produced by DR 4.3.1. Detection of microaneurysms 4.3.2. Detection of hemorrhages 4.3.3. Detection of exudates 4.4. Implementation of an Expert System 5. Current State of Art of Comprehensive Automated Systems for DR Detection 6. Conclusion References Chapter 17 Motion Correction Techniques for MR-Guided HIFU Ablation of Abdominal Organs 1. Introduction 2. Motion Correction for Real Time MR-thermometry and MR-dosimetry 2.1. Gated Acquisition Strategies 2.2. Non-gated Acquisition Strategies 2.2.1. Non-gated MR thermometry 2.2.2. Non-gated MR dosimetry 3. Motion Correction for Real-time Beam Steering 3.1. Beam Steering using Indirect Motion Estimation 3.1.1. Surrogate of the target motion 3.1.2. Training data 3.1.3. Motion model 3.2. Beam Steering Using Direct Motion Estimation 3.2.1. Direct motion estimation using MRI 3.2.2. Direct motion estimation using US signal 4. Conclusions References Chapter 18 Advanced Imaging Technologies in Proton Therapy 1. Radiation Therapy 1.1. High-Energy Photon Radiation Therapy 1.2. Proton Radiation Therapy 2. Proton Interactions with Matter 2.1. Positron Emissions 2.2. Prompt Gamma Ray Emissions 3. Range Uncertainty in Proton Therapy 3.1. Uncertainty in CT Number-to-Proton-Relative-Stopping-Power Calibration 3.2. Anatomical Changes and Daily Setup Uncertainty 4. Advanced Imaging Technologies Addressing the Range Uncertainty 4.1. Range Uncertainty and its Impacts 4.2. Positron Emission Tomography 4.2.1. In-beam PET 4.2.2. In-room PET 4.2.3. Off-line PET 4.2.4. Method comparisons and potential improvements 4.3. Prompt Gamma Ray Imaging 4.3.1. Pinhole camera 4.3.2. Compton camera 4.3.2.1. Electron-tracking compton camera 4.3.2.2. Three-stage Compton camera 4.3.3. Knife-edge-slit gamma camera 4.4. Proton Computed Tomography 4.5. Other Proton-Range Verification Techniques 5. Summary References Chapter 19 An Automated Robust Segmentation Method for Intravascular Ultrasound Images 1. Background/Literature Survey 2. Preliminary Data and Studies 2.1. Preliminary Data and Information 2.2. Preliminary Study Results 3. The Meta-algorithm 4. Quantitative Performance Evaluation 5. Concluding Remarks and Areas for Further Work References Chapter 20 Computational Methods for the Analysis of Intravascular Ultrasound Data 1. Introduction 1.1. Intravascular Ultrasound 1.2. IVUS Data Analysis 1.2.1. IVUS segmentation 1.2.2. Plaque characterization 1.2.3. Extra-luminal blood perfusion detection 1.3. Non-linear IVUS 1.4. Differential Imaging 2. Conclusions Acknowledgements References Chapter 21 MEMS-based Transducers (CMUT) For Medical Ultrasound Imaging 1. Introduction 2. CMUT Array Design, Microfabrication and Packaging 2.1. Array Design 2.2. Microfabrication 2.3. Packaging 3. Probe Engineering and System Integration 4. Probe Characterization and Ultrasound Imaging 4.1. Electrical Impedance 4.2. Pulse-echo 4.3. Transmission 4.4. Ultrasound Imaging 5. Discussion and Conclusion References Chapter 22 Advances in Picture Archiving Communication System and Medical Imaging Cloud 1. Introduction 2. PACS System 2.1. DICOM Model 2.2. Medical Imaging and PACS 2.3. Multimodality Imaging and PACS 2.4. Cardiovascular Imaging PACS 2.5. PACS and Models 3. Medical Imaging Cloud 3.1. Cloud Model 3.2. Case Study of Citrix Based Cloud 3.3. Zero-footprint Client 3.4. Vendor Neutral Archive 4. Considerations 4.1. Security and Performance 4.2. Structured Report 4.3. Compression 5. Conclusion References Index