Читать книгу Machine Learning for Tomographic Imaging - Professor Ge Wang - Страница 4
Contents
ОглавлениеPart I Background
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
1.1.5 Data decorrelation and whitening
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
2.3 CT reconstruction via dictionary learning
2.3.1 Statistic iterative reconstruction framework (SIR)
2.3.2 Dictionary-based low-dose CT reconstruction
3 Artificial neural networks
3.1 Basic concepts
3.1.1 Biological neural network
3.1.4 Discrete convolution and weights
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
3.3 Typical artificial neural networks
Part II X-ray computed tomography
4 X-ray computed tomography
4.1.1 Projection
4.2.1 Fourier transform
4.2.3 Parallel-beam image reconstruction
4.2.4 Fan-beam image reconstruction
4.2.5 Cone-beam image 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
4.4.1 CT scanning modes
4.4.3 The latest progress in CT technology
5 Deep CT reconstruction
5.1 Introduction
5.3 Data domain and hybrid processing
5.4 Iterative reconstruction combined with deep learning
5.4.3 Learned primal–dual reconstruction
5.5 Direct reconstruction via deep learning
Part III Magnetic resonance imaging
6 Classical methods for MRI reconstruction
6.2 Fast sampling and image reconstruction
6.2.2 Total variation regularization
6.3.3 TV regularized pMRI reconstruction
7 Deep-learning-based MRI reconstruction
7.1 Structured deep MRI reconstruction networks
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
7.3.3 Synergized pulsing-imaging network
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
Part IV Others
8 Modalities and integration
8.1 Nuclear emission tomography
8.1.2 Network-based emission tomography
8.2.2 Network-based ultrasound imaging
8.3.1 Interferometric and diffusive imaging
8.3.2 Network-based optical imaging
9 Image quality assessment
10 Quantum computing*
A Math and statistics basics
B Hands-on networks