# Knowledge-driven deep learning for fast MR imaging: undersampled MR image reconstruction from supervised to un-supervised learning

arXiv: https://arxiv.org/abs/2402.02704 · HTML: https://mlsanity.com/paper/2402.02704/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2024.

## Built on by (most cited first, 11 of 11)

- [Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation](https://mlsanity.com/paper/2508.09177/) (2025)
- [MRI Super-Resolution with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2511.16854/) (2025)
- [Diff5T: Benchmarking Human Brain Diffusion MRI with an Extensive 5.0 Tesla K-Space and Spatial Dataset](https://mlsanity.com/paper/2412.06666/) (2024)
- [Generative Autoregressive Transformers for Model-Agnostic Federated MRI Reconstruction](https://mlsanity.com/paper/2502.04521/) (2025)
- [RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling under Continuous Representation](https://mlsanity.com/paper/2408.01944/) (2024)
- [MEDMKG: Benchmarking Medical Knowledge Exploitation with Multimodal Knowledge Graph](https://mlsanity.com/paper/2505.17214/) (2025)
- [A Diffusion-Driven Temporal Super-Resolution and Spatial Consistency Enhancement Framework for 4D MRI imaging](https://mlsanity.com/paper/2506.04116/) (2025)
- [SamRobNODDI: Q-Space Sampling-Augmented Continuous Representation Learning for Robust and Generalized NODDI](https://mlsanity.com/paper/2411.06444/) (2024)
- [3D Anatomical Structure-guided Deep Learning for Accurate Diffusion Microstructure Imaging](https://mlsanity.com/paper/2502.17933/) (2025)
- [Two-stage deep learning framework for the restoration of incomplete-ring PET images](https://mlsanity.com/paper/2504.00816/) (2025)
- [Stochastic Finite-Transition Learning and Exploration for Accelerated MRI Reconstruction](https://mlsanity.com/paper/2610.13095/) (2026)

## Abstract (arXiv)

Deep learning (DL) has emerged as a leading approach in accelerating MR imaging. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MR imaging involves physics-based imaging processes, unique data properties, and diverse imaging tasks. This domain knowledge needs to be integrated with data-driven approaches. Our review will introduce the significant challenges faced by such knowledge-driven DL approaches in the context of fast MR imaging along with several notable solutions, which include learning neural networks and addressing different imaging application scenarios. The traits and trends of these techniques have also been given which have shifted from supervised learning to semi-supervised learning, and finally, to unsupervised learning methods. In addition, MR vendors' choices of DL reconstruction have been provided along with some discussions on open questions and future directions, which are critical for the reliable imaging systems.