Machine Learning for Tomographic Imaging. Professor Ge Wang

       3.3.3 ResNet

       3.3.4 GANs

       3.3.5 RNNs

       3.3.6 GCNs*

       References

       Part II X-ray computed tomography

       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

       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

       Part III Magnetic resonance imaging

       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

       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