# Data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies

arXiv: https://arxiv.org/abs/2401.16564 · HTML: https://mlsanity.com/paper/2401.16564/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2024.

## Built on by (most cited first, 10 of 10)

- [MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimation](https://mlsanity.com/paper/2402.18451/) (2024)
- [Disentangled Learning Improves Implicit Neural Representations for Medical Reconstruction](https://mlsanity.com/paper/2605.04234/) (2026)
- [Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approach](https://mlsanity.com/paper/2503.05063/) (2025)
- [Self-supervised Deep Unrolled Model with Implicit Neural Representation Regularization for Accelerating MRI Reconstruction](https://mlsanity.com/paper/2510.06611/) (2025)
- [Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba](https://mlsanity.com/paper/2405.17659/) (2024)
- [Bridging Synthetic-to-Real Gaps: Frequency-Aware Perturbation and Selection for Single-shot Multi-Parametric Mapping Reconstruction](https://mlsanity.com/paper/2503.03475/) (2025)
- [Deep Generative Prior for First Order Inverse Optimization](https://mlsanity.com/paper/2504.20278/) (2025)
- [Enhancing Diffusion-Weighted Images (DWI) for Diffusion MRI: Is it Enough without Non-Diffusion-Weighted B=0 Reference?](https://mlsanity.com/paper/2505.12978/) (2025)
- [MoE-dqINR: A Unified Mixture-of-Experts Implicit Neural Representation Framework for Scan-Specific Dynamic and Quantitative MRI Reconstruction](https://mlsanity.com/paper/2605.31302/) (2026)
- [DeepCormack: Fermi surface tomography using model-based data-driven algorithms](https://mlsanity.com/paper/2607.13107/) (2026)

## Abstract (arXiv)

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.