A Review on Deep Learning in Medical Image Reconstruction

Abstract

Medical imaging is crucial in modern clinics to guide the diagnosis and treatment of diseases. Medical image reconstruction is one of the most fundamental and important components of medical imaging, whose major objective is to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients. Mathematical models in medical image reconstruction or, more generally, image restoration in computer vision, have been playing a prominent role. Earlier mathematical models are mostly designed by human knowledge or hypothesis on the image to be reconstructed, and we shall call these models handcrafted models. Later, handcrafted plus data-driven modeling started to emerge which still mostly relies on human designs, while part of the model is learned from the observed data. More recently, as more data and computation resources are made available, deep learning based models (or deep models) pushed data-driven modeling to the extreme where the models are mostly based on learning with minimal human designs. Both handcrafted and data-driven modeling have their own advantages and disadvantages. One of the major research trends in medical imaging is to combine handcrafted modeling with deep modeling so that we can enjoy benefits from both approaches. The major part of this article is to provide a conceptual review of some recent works on deep modeling from the unrolling dynamics viewpoint. This viewpoint stimulates new designs of neural network architectures with inspiration from optimization algorithms and numerical differential equations. Given the popularity of deep modeling, there are still vast remaining challenges in the field, as well as opportunities which we shall discuss at the end of this article.

31 pages, 6 figures. Survey paper. Revise the typos

References in corpus (16)

• PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network
• Shake-Shake regularization
• The Reversible Residual Network: Backpropagation Without Storing Activations
• Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis
• Why Deep Neural Networks for Function Approximation?
• MgNet: A Unified Framework of Multigrid and Convolutional Neural Network
• Understanding and Improving Transformer From a Multi-Particle Dynamic System Point of View
• ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI
• Nonlinear Approximation via Compositions
• You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
• A Priori Estimates of the Population Risk for Residual Networks
• Deep Residual Learning and PDEs on Manifold
• Differentiable Linearized ADMM
• Stochastic Training of Residual Networks: a Differential Equation Viewpoint
• Multiscale Adaptive Representation of Signals: I. The Basic Framework
• Towards Robust ResNet: A Small Step but A Giant Leap

Cited by in corpus (5)

• Deep Learning for Biomedical Image Reconstruction: A Survey
• NESTANets: Stable, accurate and efficient neural networks for analysis-sparse inverse problems
• Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives
• Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging
• Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation