Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies
arXiv:2401.16564 · doi:10.1109/RBME.2024.3485022
Abstract
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction.
Accepted by IEEE Reviews in Biomedical Engineering (RBME)
References in corpus (24)
- MoDL: Model Based Deep Learning Architecture for Inverse Problems
- Domain Adaptation for Medical Image Analysis: A Survey
- On instabilities of deep learning in image reconstruction - Does AI come at a cost?
- Deep Image Prior
- Deep learning for undersampled MRI reconstruction
- Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference Data
- Plug and play methods for magnetic resonance imaging (long version)
- Adaptive Diffusion Priors for Accelerated MRI Reconstruction
- RARE: Image Reconstruction using Deep Priors Learned without Ground Truth
- A Deep Information Sharing Network for Multi-contrast Compressed Sensing MRI Reconstruction
- J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction
- High-Frequency Space Diffusion Models for Accelerated MRI
- Transform Learning for Magnetic Resonance Image Reconstruction: From Model-based Learning to Building Neural Networks
- Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models
- Linear Predictability in MRI Reconstruction: Leveraging Shift-Invariant Fourier Structure for Faster and Better Imaging
- B-spline Parameterized Joint Optimization of Reconstruction and K-space Trajectories (BJORK) for Accelerated 2D MRI
- Wasserstein GANs for MR Imaging: from Paired to Unpaired Training
- OEDIPUS: An Experiment Design Framework for Sparsity-Constrained MRI
- Active MR k-space Sampling with Reinforcement Learning
- Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI
- Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking
- ENSURE: A General Approach for Unsupervised Training of Deep Image Reconstruction Algorithms
- Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI
- AutoSamp: Autoencoding k-space Sampling via Variational Information Maximization for 3D MRI