Machine learning represents a paradigm shift in tomographic imaging, and image reconstruction is a new frontier of machine learning. This book will meet the needs of those who want to catch the wave of smart imaging. The book targets graduate students and researchers in the imaging community. Open network software, working datasets, and multimedia will be included. The first of its kind in the emerging field of deep reconstruction and deep imaging, Machine Learning for Tomographic Imaging presents the most essential elements, latest progresses and an in-depth perspective on this important topic.
Author(s): Ge Wang, Yi Zhang, Xiaojing Ye, Xuanqin Mou
Series: IPEM–IOP Series in Physics and Engineering in Medicine and Biology
Publisher: IOP Publishing
Year: 2020
Language: English
Pages: 409
City: Bristol
PRELIMS.pdf
Foreword
Preface
Acknowledgments
Author biographies
Ge Wang
Yi Zhang
Xiaojing Ye
Xuanqin Mou
Introduction
Outline placeholder
0.1 Artificial intelligence/machine learning/deep learning
0.2 Image analysis versus image reconstruction
0.3 Analytic/iterative/deep learning algorithms for tomographic reconstruction
0.4 The field of deep reconstruction and the need for this book
0.5 The organization of this book
0.6 More to learn and what to expect next
References
CH001.pdf
Chapter 1 Background knowledge
1.1 Imaging principles and a priori information
1.1.1 Overview
1.1.2 Radon transform and non-ideality in data acquisition
1.1.3 Bayesian reconstruction
1.1.4 The human vision system
1.1.5 Data decorrelation and whitening
1.1.6 Sparse coding
References
CH002.pdf
Chapter 2 Tomographic reconstruction based on a learned dictionary
2.1 Prior information guided reconstruction
2.2 Single-layer neural network
2.2.1 Matching pursuit algorithm
2.2.2 The K-SVD algorithm
2.3 CT reconstruction via dictionary learning
2.3.1 Statistic iterative reconstruction framework (SIR)
2.3.2 Dictionary-based low-dose CT reconstruction
2.4 Final remarks
References
CH003.pdf
Chapter 3 Artificial neural networks
3.1 Basic concepts
3.1.1 Biological neural network
3.1.2 Neuron models
3.1.3 Activation function
3.1.4 Discrete convolution and weights
3.1.5 Pooling strategy
3.1.6 Loss function
3.1.7 Backpropagation algorithm
3.1.8 Convolutional neural network
3.2 Training, validation, and testing of an artificial neural network
3.2.1 Training, validation, and testing datasets
3.2.2 Training, validation, and testing processes
3.2.3 Related concepts
3.3 Typical artificial neural networks
3.3.1 VGG network
3.3.2 U-Net
3.3.3 ResNet
3.3.4 GANs
3.3.5 RNNs
3.3.6 GCNs*
References
CH004.pdf
Chapter 4 X-ray computed tomography
4.1 X-ray data acquisition
4.1.1 Projection
4.1.2 Backprojection
4.1.3 (Back)Projector
4.2 Analytical reconstruction
4.2.1 Fourier transform
4.2.2 Central slice theorem
4.2.3 Parallel-beam image reconstruction
4.2.4 Fan-beam image reconstruction
4.2.5 Cone-beam image reconstruction∗
4.3 Iterative reconstruction
4.3.1 Linear equations
4.3.2 Algebraic iterative reconstruction
4.3.3 Statistical iterative reconstruction
4.3.4 Regularized iterative reconstruction∗
4.3.5 Model-based iterative reconstruction
4.4 CT scanner
4.4.1 CT scanning modes
4.4.2 Detector technology
4.4.3 The latest progress in CT technology
4.4.4 Practical applications
References
CH005.pdf
Chapter 5 Deep CT reconstruction
5.1 Introduction
5.2 Image domain processing
5.2.1 RED-CNN
5.2.2 AAPM-Net
5.2.3 WGAN-VGG
5.3 Data domain and hybrid processing
5.4 Iterative reconstruction combined with deep learning
5.4.1 LEARN
5.4.2 3pADMM
5.4.3 Learned primal–dual reconstruction
5.5 Direct reconstruction via deep learning
References
CH006.pdf
Chapter 6 Classical methods for MRI reconstruction
6.1 The basic physics of MRI
6.2 Fast sampling and image reconstruction
6.2.1 Compressed sensing MRI
6.2.2 Total variation regularization
6.2.3 ADMM and primal–dual
6.3 Parallel MRI*
6.3.1 GRAPPA
6.3.2 SENSE
6.3.3 TV regularized pMRI reconstruction
References
CH007.pdf
Chapter 7 Deep-learning-based MRI reconstruction
7.1 Structured deep MRI reconstruction networks
7.1.1 ISTA-Net
7.1.2 ADMM-Net
7.1.3 Variational reconstruction network
7.2 Leveraging generic network structures
7.2.1 Cascaded CNNs
7.2.2 GAN-based reconstruction networks
7.3 Methods for advanced MRI technologies
7.3.1 Dynamic MRI
7.3.2 MR fingerprinting
7.3.3 Synergized pulsing-imaging network
7.4 Miscellaneous topics*
7.4.1 Optimization with complex variables and Wirtinger calculus
7.4.2 Activation functions with complex variables
7.4.3 Optimal k-space sampling
7.5 Further readings
References
CH008.pdf
Chapter 8 Modalities and integration
8.1 Nuclear emission tomography
8.1.1 Emission data models
8.1.2 Network-based emission tomography
8.2 Ultrasound imaging
8.2.1 Ultrasound scans
8.2.2 Network-based ultrasound imaging
8.3 Optical imaging
8.3.1 Interferometric and diffusive imaging
8.3.2 Network-based optical imaging
8.4 Integrated imaging
8.5 Final remarks
References
CH009.pdf
Chapter 9 Image quality assessment
9.1 General measures
9.1.1 Classical distances
9.1.2 Structural similarity
9.1.3 Information measures
9.2 System-specific indices
9.3 Task-specific performance
9.4 Network-based observers*
9.5 Final remarks*
References
CH010.pdf
Chapter 10 Quantum computing*
10.1 Wave–particle duality
10.2 Quantum gates
10.3 Quantum algorithms
10.4 Quantum machine learning
10.5 Final remarks
References
APP1.pdf
Chapter
A.1 Numerical optimization
A.1.1 Basics in optimization
A.1.2 Unconstrained optimization algorithms
A.1.3 Stochastic gradient descent methods
A.1.4 Theory of constrained optimization
A.2 Statistical inferences
A.3 Information theory
A.3.1 Entropy
A.3.2 Mutual information
A.3.3 Kullback–Leibler divergence
References
APP2.pdf
Chapter
B.1 Open source toolkits for deep learning
B.2 Datasets for deep learning
B.2.1 Datasets of natural images
B.2.2 Datasets of medical images
B.3 Network models for deep reconstruction
References
