mlsanityData to 7 Oct 2026

Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging

arXiv 2203.12215 ↗First posted 23 Mar 2022eess.IV cs.CV cs.LGcited by 49

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Abstract (arXiv)

Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits. This article provides an overview of the recent developments in incorporating physics information into learning-based MRI reconstruction. We consider inverse problems with both linear and non-linear forward models for computational MRI, and review the classical approaches for solving these. We then focus on physics-driven deep learning approaches, covering physics-driven loss functions, plug-and-play methods, generative models, and unrolled networks. We highlight domain-specific challenges such as real- and complex-valued building blocks of neural networks, and translational applications in MRI with linear and non-linear forward models. Finally, we discuss common issues and open challenges, and draw connections to the importance of physics-driven learning when combined with other downstream tasks in the medical imaging pipeline.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). Builds on 49 of its 111 references that are in the map. First posted on arXiv 23 Mar 2022.

Builds on

49 of 111 references.

References · 49

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37 more
  1. 2017Deep learning for undersampled MRI reconstructioncited by 91
  2. 2019Deep Learning Methods for Parallel Magnetic Resonance Image Reconstructioncited by 81
  3. 2019Plug and play methods for magnetic resonance imaging (long version)cited by 75
  4. 2019Time-Dependent Deep Image Prior for Dynamic MRIcited by 70
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 11; 2024: 10; 2025: 22; 2026: 4.

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  1. 2022A theoretical framework for self-supervised MR image reconstruction using sub-sampling via variable density Noisier2Noisecited by 29
  2. 2024MambaMIR: An Arbitrary-Masked Mamba for Joint Medical Image Reconstruction and Uncertainty Estimationcited by 19
  3. 2023Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Reviewcited by 18
  4. 2023One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstructioncited by 14
  5. 2023Learning Fourier-Constrained Diffusion Bridges for MRI Reconstructioncited by 13
  6. 2024Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse Problemscited by 9
  7. 2023Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fieldscited by 8
  8. 2024Task-Driven Uncertainty Quantification in Inverse Problems via Conformal Predictioncited by 8
  9. 2023PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstructioncited by 7
  10. 2024Physics-Driven Autoregressive State Space Models for Medical Image Reconstructioncited by 7
  11. 2025MRI Super-Resolution with Deep Learning: A Comprehensive Surveycited by 7
  12. 2023K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsetscited by 5
37 more
  1. 2023Robust Physics-based Deep MRI Reconstruction Via Diffusion Purificationcited by 4
  2. 2025PISCO: Self-Supervised k-Space Regularization for Improved Neural Implicit k-Space Representations of Dynamic MRIcited by 4
  3. 2025Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participantscited by 4
  4. 2025PnP-CM: Consistency Models as Plug-and-Play Priors for Inverse Problemscited by 4
  5. 2023Adapting model-based deep learning to multiple acquisition conditions: Ada-MoDLcited by 3
  6. 2025Deep learning of personalized priors from past MRI scans enables fast, quality-enhanced point-of-care MRI with low-cost systemscited by 3
  7. 2022Denoising Generalized Expectation-Consistent Approximation for MR Image Recoverycited by 2
  8. 2023Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitzcited by 2
  9. 2023Non-Cartesian Self-Supervised Physics-Driven Deep Learning Reconstruction for Highly-Accelerated Multi-Echo Spiral fMRIcited by 2
  10. 2024Self-Supervised k-Space Regularization for Motion-Resolved Abdominal MRI Using Neural Implicit k-Space Representationcited by 2
  11. 2024Point Neuron Learning: A New Physics-Informed Neural Network Architecturecited by 2
  12. 2024Fast MRI for All: Bridging Access Gaps by Training without Raw Datacited by 2
  13. 2025An unsupervised method for MRI recovery: Deep image prior with structured sparsitycited by 2
  14. 2025Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problemscited by 2
  15. 2025Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retrainingcited by 2
  16. 2025Time-Embedded Algorithm Unrolling for Computational MRIcited by 2
  17. 2024Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstructioncited by 1
  18. 2025Rational Gaussian wavelets and corresponding model driven neural networkscited by 1
  19. 2025Lightweight Hypercomplex MRI Reconstruction: A Generalized Kronecker-Parameterized Approachcited by 1
  20. 2025Deep Learning Assisted Outer Volume Removal for Highly-Accelerated Real-Time Dynamic MRIcited by 1
  21. 2025From Coarse to Continuous: Progressive Refinement Implicit Neural Representation for Motion-Robust Anisotropic MRI Reconstructioncited by 1
  22. 2025CUTE-MRI: Conformalized Uncertainty-based framework for Time-adaptivE MRIcited by 1
  23. 2025Self-supervised Deep Unrolled Model with Implicit Neural Representation Regularization for Accelerating MRI Reconstructioncited by 1
  24. 2025Equivariant Deep Equilibrium Models for Imaging Inverse Problemscited by 1
  25. 2023Meta-Prior: Meta learning for Adaptive Inverse Problem Solverscited by 0
  26. 2024Motion-Informed Deep Learning for Brain MR Image Reconstruction Frameworkcited by 0
  27. 2024Real-time cardiac cine MRI -- A comparison of a diffusion probabilistic model with alternative state-of-the-art image reconstruction techniques for undersampled spiral acquisitionscited by 0
  28. 2025Training-Free Adversarial Robustness in Computational MRIcited by 0
  29. 2025Evaluating structural uncertainty in accelerated MRI: are voxelwise measures useful surrogates?cited by 0
  30. 2025Efficient Noise Calculation in Deep Learning-based MRI Reconstructionscited by 0
  31. 2025Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Studycited by 0
  32. 2025Dynamically Weighted Momentum with Adaptive Step Sizes for Efficient Deep Network Trainingcited by 0
  33. 2025Minimax Multi-Target Conformal Prediction with Applications to Imaging Inverse Problemscited by 0
  34. 2026Accelerating Stroke MRI with Diffusion Probabilistic Models through Large-Scale Pre-training and Target-Specific Fine-Tuningcited by 0
  35. 2026NexOP: Joint Optimization of NEX-Aware k-space Sampling and Image Reconstruction for Low-Field MRIcited by 0
  36. 2026Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priorscited by 0
  37. 2026Consistency Models for Fast MRI Reconstruction Using Regularization by Denoisingcited by 0