Medical Image Synthesis: Methods and Clinical Applications (Imaging in Medical Diagnosis and Therapy)

✍ Scribed by Xiaofeng Yang (editor)

• Publisher: CRC Press
• Year: 2023
• Tongue: English
• Leaves: 318
• Edition: 1
• Category: Library

✦ Synopsis

Image synthesis across and within medical imaging modalities is an active area of research with broad applications in radiology and radiation oncology. This book covers the principles and methods of medical image synthesis, along with state-of-the-art research.

First, various traditional non-learning-based, traditional machine-learning-based, and recent deep-learning-based medical image synthesis methods are reviewed. Second, specific applications of different inter- and intra-modality image synthesis tasks and of synthetic image-aided segmentation and registration are introduced and summarized, listing and highlighting the proposed methods, study designs, and reported performances with the related clinical applications of representative studies. Third, the clinical usages of medical image synthesis, such as treatment planning and image-guided adaptive radiotherapy, are discussed. Last, the limitations and current challenges of various medical synthesis applications are explored, along with future trends and potential solutions to solve these difficulties.

The benefits of medical image synthesis have sparked growing interest in a number of advanced clinical applications, such as magnetic resonance imaging (MRI)-only radiation therapy treatment planning and positron emission tomography (PET)/MRI scanning. This book will be a comprehensive and exciting resource for undergraduates, graduates, researchers, and practitioners.

✦ Table of Contents

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
List of Contributors
Introduction
Section I: Methods and Principles
Chapter 1: Non-Deep-Learning-Based Medical Image Synthesis Methods
1.1 Introduction
1.2 Overview of Non-learning-based Methods
1.2.1 Single-Atlas-Based Method
1.2.2 Multi-atlas-based Method
1.2.3 Patch-based Atlas Method
1.3 Overview of Traditional Machine-Learning-based Methods – Voxel-based Techniques
1.4 Discussion
1.4.1 Achievements
1.4.2 Limitations
1.5 Conclusion
References
Chapter 2: Deep-Learning-Based Medical Image Synthesis Methods
2.1 Introduction
2.2 Literature Searching
2.3 Network Architecture
2.3.1 NN
2.3.2 CNN
2.3.3 FCN
2.3.4 GAN
2.3.4.1 Conditional GAN
2.3.4.1.1 DCGAN
2.3.4.1.2 Pix2pix
2.3.4.1.3 InfoGAN
2.3.4.2 Cycle-GAN
2.3.4.2.1 Res-Cycle-GAN
2.3.4.2.2 Dense-Cycle-GAN
2.3.4.2.3 Unsupervised Image-to-Image Translation Networks (UNIT)
2.3.4.2.4 Bicycle-GAN
2.3.4.2.5 StarGAN
2.3.5 Loss Function
2.3.5.1 Image Distance Loss
2.3.5.2 Histogram Matching Loss
2.3.5.3 Perceptual Loss
2.3.5.4 Discriminator Loss
2.3.5.5 Adversarial Loss
2.4 Applications
2.4.1 Multimodality MRI Synthesis
2.4.2 MRI-only Radiation Therapy Treatment Planning
2.4.3 CBCT Improvement/Enhancement
2.4.4 Low-count PET and PET Attenuation Correction
2.5 Summary and Discussion
Disclosures
References
Section II: Applications of Inter-Modality Image Synthesis
Chapter 3: MRI-Based Image Synthesis
3.1 Introduction
3.1.1 Synthetic CT Quality
3.1.2 MR-only Radiation Therapy
3.1.3 PET Attenuation Correction
3.1.4 Discussion
References
Chapter 4: CBCT/CT-Based Image Synthesis
4.1 Synthetic CT from CBCT Images
4.2 Synthetic MRI from CT/CBCT Images
4.3 Synthetic DECT from Single-Energy CT
4.4 Discussion
References
Chapter 5: CT-Based DVF/Ventilation/Perfusion Imaging
5.1 Introduction
5.2 CT-based DVF Imaging (CTDI)
5.2.1 Conventional CTDI Methods
5.2.2 Deep Learning-based CTDI Methods
5.2.2.1 Supervised DVF Synthesis Network
5.2.2.2 Unsupervised DVF Synthesis Network
5.3 CT-based Ventilation Imaging (CTVI)
5.3.1 Classical DIR-based CTVI Methods
5.3.1.1 HU-based Method
5.3.1.2 Volume-based Method
5.3.2 Improved DIR-based CTVI Methods
5.3.2.1 Hybrid Method
5.3.2.2 Biomechanical Model
5.3.2.3 Integrated Jacobian Formulation (IJF) Method
5.3.2.4 Mass-Conserving Volume Change (MCVC) Method
5.3.2.5 Multilayer Supervoxels Estimation Method
5.3.3 Other CTVI Methods
5.3.3.1 Attenuation Method
5.3.3.2 Deep Learning Method
5.4 CT-based Perfusion Imaging (CTPI)
5.4.1 DIR-based CTPI Methods
5.4.2 Deep Learning-based CTPI Methods
5.4.3 CTPI Techniques for Other Anatomies
5.5 Clinical Applications of CT-based DVF/Ventilation/Perfusion Imaging
5.6 Concluding Remarks
References
Chapter 6: Imaged-Based Dose Planning Prediction
6.1 Introduction
6.2 Status
6.3 Current Challenges and Future Perspectives
References
Section III: Applications of Intra-Modality Image Synthesis
Chapter 7: Medical Imaging Denoising
7.1 Introduction
7.2 Review of Medical Image Denoising Applications
7.2.1 Image Denoising Problem Statement
7.2.2 Denoising Methods
7.2.2.1 Classical (Spatial Domain) Denoising Method
7.2.2.1.1 Spatial Domain Filtering
7.2.2.1.2 Variational Denoising Methods
7.2.2.1.2.1 Total Variation (TV) Regularization
7.2.2.1.2.2 Nonlocal Regularization
7.2.2.1.2.3 Sparse Representation
7.2.2.1.2.4 Low-rank Minimization
7.2.2.2 Transform Techniques
7.2.2.2.1 Transform Domain Filtering
7.2.2.2.2 Data-adaptive Transform
7.2.2.2.2.1 Independent Component Analysis (ICA)
7.2.2.2.2.2 Principal Component Analysis (PCA)
7.2.2.2.3 Non-data-adaptive Transform
7.2.2.2.3.1 Spatial-frequency Domain
7.2.2.2.3.2 Wavelet Domain
7.2.2.2.4 Block-Matching and 3D (BM3D) Filtering
7.2.2.3 Deep Learning (DL)-based Image Denoising Methods
7.3 Convolutional Neuron Network (CNN) for Medical Image Denoising and Resolution Restoration
7.4 Different Medical Image Modalities
7.4.1 Computed Tomography (CT)
7.4.1.1 Reconstruction
7.4.1.1.1 Filtered Back Projection
7.4.1.1.2 Iterative Reconstruction
7.4.2 Magnetic Resonance Imaging (MRI)
7.4.2.1 MRI Denoising Approaches
7.4.3 Positron Emission Tomography
7.4.3.1 PET Denoising Approaches
7.4.4 Ultrasound
7.4.4.1 US Denoising Approaches
7.5 Deep Learning Approaches for CT Denoising
7.5.1 Convolutional Neuron Network Approached Design Optimization for Medical Image Denoising
7.5.2 STIR-Net: Spatial-Temporal Image Restoration Net for CT Perfusion Radiation Reduction
7.6 Summary and Discussion
Acknowledgment
References
Chapter 8: Attenuation Correction for Quantitative PET/MR Imaging
8.1 Introduction
8.2 Methods for PET Attenuation Correction
8.2.1 Atlas-based Methods
8.2.2 Segmentation-based Methods
8.2.3 Joint Estimation-based Methods
8.2.4 Machine Learning-based Methods
8.2.5 Deep Learning-based Methods
8.3 Conclusion
References
Chapter 9: High-Resolution Image Estimation using Deep Learning
9.1 Introduction
9.2 Methods
9.2.1 Self-supervised Learning Framework
9.2.2 The cycleGAN
9.3 Results
9.3.1 Ultrasound High-resolution Image Estimation
9.3.1.1 Breast US
9.3.1.2 Prostate US
9.3.2 CT High-resolution Image Estimation
9.3.3 MRI High-resolution Image Estimation
9.4 Discussion
9.5 Conclusion
References
Chapter 10: 2D–3D Transformation for 3D Volumetric Imaging
10.1 Introduction
10.2 Methods
10.2.1 Overview of the Deep-Learning-Based 2D–3D Transformation Methods
10.2.2 Network Training
10.2.3 Supervision and Loss Function
10.3 Evaluation Results
10.4 Discussion
References
Chapter 11: Multimodality MRI Synthesis
11.1 Introduction
11.2 Multimodality MRI Synthesis via Patch-Based Conventional Learning
11.2.1 A Dual-Domain Cascaded Regression for 7T MRI Prediction from 3T MRI
11.2.2 An Evaluation of Different Patch-Based Conventional Learning Methods
11.3 Multimodality MRI Synthesis via Deep Learning
11.3.1 A Fully Supervised 7T MRI Prediction from 3T MRI with CNNs
11.3.1.1 Method
11.3.1.2 Performance Evaluation
11.3.2 A Semi-Supervised Adversarial Learning for 7T MRI Prediction from 3T MRI
11.3.2.1 Method
11.3.2.2 Performance Evaluation
11.4 Conclusion
References
Chapter 12: Multi-Energy CT Transformation and Virtual Monoenergetic Imaging
12.1 Introduction
12.2 Implementation of Multi-energy CT Imaging
12.2.1 Sequential Scanning
12.2.2 Fast Tube Potential Switching
12.2.3 Multilayer Detector
12.2.4 Dual-Source Acquisitions
12.2.5 Beam Filtration Techniques
12.2.6 Energy-Resolved Detector
12.3 Motivation of Artificial Intelligence-based Multi-energy CT Imaging
12.4 Energy Domain MECT Image Synthesis
12.5 Cross-Domain MECT Image Synthesis
12.6 Remaining Challenges and Future Work
12.7 Summary
Acknowledgment
References
Chapter 13: Metal Artifact Reduction
13.1 Introduction
13.2 Review of MAR in Different Modalities
13.2.1 MAR in Magnetic Resonance Imaging (MRI)
13.2.2 MAR in Computed Tomography (CT)
13.3 Supervised Dual-domain Learning for MAR
13.3.1 Overview of Dual-domain Learning Framework
13.3.2 Generation of the Prior Image
13.3.3 Deep Sinogram Completion
13.3.4 Objective Function
13.3.5 Dataset and Implementation
13.3.6 Experimental Results on DeepLesion Data
13.3.6.1 Quantitative Comparisons
13.3.6.2 Qualitative Analysis
13.3.7 Generalization to Different Site Data
13.3.8 Experiments on CT Images with Real Metal Artifacts
13.3.8.1 Results on Real Metal-Corrupted CT Images
13.3.8.2 The Influence of Metal Segmentation
13.3.9 Analysis of Our Approach
13.3.9.1 Effectiveness of Prior Image Generation
13.3.9.2 Effectiveness of Residual-Sinogram-Learning
13.3.9.3 Compared with Tissue Processing
13.4 Self-Supervised Dual-Domain MAR
13.4.1 Overview
13.4.2 Self-Supervised Cross-Domain Learning
13.4.2.1 Sinogram Completion
13.4.2.2 FBP Reconstruction Loss
13.4.2.3 Image Refinement
13.4.3 Prior-Image-Based Metal Trace Replacement
13.4.4 Training and Testing Strategies
13.4.5 Datasets and Implementation
13.4.6 Ablation Study
13.4.7 Comparison with Other Methods
13.4.8 Qualitative Analysis
13.4.9 Experiments on CT Images with Real Artifacts
13.4.10 Experiments on Different Site Data
13.4.11 Comparison with More Recent Methods
13.5 Discussion and Conclusion
References
Section IV: Other Applications of Medical Image Synthesis
Chapter 14: Synthetic Image-Aided Segmentation
14.1 Introduction
14.2 Modality Enhancement-Based Segmentation
14.2.1 Multi-Modality Image Synthesis
14.2.2 Similar-Modality Image Synthesis
14.3 Training Data Enlargement-Based Segmentation
14.4 Summary and Discussion
Disclosures
References
Chapter 15: Synthetic Image-Aided Registration
References
Chapter 16: CT Image Standardization Using Deep Image Synthesis Models
16.1 Introduction
16.2 Background
16.2.1 CT Image Acquisition and Reconstruction Parameters
16.2.2 Radiomic Features
16.2.3 Deep Generative Models for Image Synthesis
16.2.3.1 U-net
16.2.3.2 Generative Adversarial Network
16.3 CT Image Standardization Model
16.3.1 CNN-based CT Image Standardization
16.3.2 GAN-based CT Image Standardization
16.3.2.1 GANai
16.3.2.2 STAN-CT
16.3.2.3 RadiomicGAN
16.3.2.4 CVH-CT
16.4 Discussion and Conclusion
References
Section V: Clinic Usage of Medical Image Synthesis
Chapter 17: Image-Guided Adaptive Radiotherapy
17.1 Introduction
17.2 CBCT-based Online Adaptive Radiation Therapy System
17.3 MR-Guided Real-Time Adaptive Radiation Therapy
17.4 CBCT-Guided Adaptive Proton Therapy
17.5 Online Adaptive RT Using Plan-of-the-Day
17.6 Dose Gradient Adaptation
References
Section VI: Perspectives
Chapter 18: Validation and Evaluation Metrics
18.1 Introduction
18.2 Overview of Qualitative Validation
18.3 Overview of Quantitative Validation
18.3.1 Similarity Measures
18.3.2 Dice Measures
18.3.3 Dosimetric Agreement
References
Chapter 19: Limitations and Future Trends
References
Index