mlsanityData to 7 Oct 2026

Towards performant and reliable undersampled MR reconstruction via diffusion model sampling

arXiv 2203.04292 ↗First posted 8 Mar 2022eess.IV cs.CV cs.LGcited by 59

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Magnetic Resonance (MR) image reconstruction from under-sampled acquisition promises faster scanning time. To this end, current State-of-The-Art (SoTA) approaches leverage deep neural networks and supervised training to learn a recovery model. While these approaches achieve impressive performances, the learned model can be fragile on unseen degradation, e.g. when given a different acceleration factor. These methods are also generally deterministic and provide a single solution to an ill-posed problem; as such, it can be difficult for practitioners to understand the reliability of the reconstruction. We introduce DiffuseRecon, a novel diffusion model-based MR reconstruction method. DiffuseRecon guides the generation process based on the observed signals and a pre-trained diffusion model, and does not require additional training on specific acceleration factors. DiffuseRecon is stochastic in nature and generates results from a distribution of fully-sampled MR images; as such, it allows us to explicitly visualize different potential reconstruction solutions. Lastly, DiffuseRecon proposes an accelerated, coarse-to-fine Monte-Carlo sampling scheme to approximate the most likely reconstruction candidate. The proposed DiffuseRecon achieves SoTA performances reconstructing from raw acquisition signals in fastMRI and SKM-TEA. Code will be open-sourced at www.github.com/cpeng93/DiffuseRecon.

Key facts. Cited by 59 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 16 of its 25 references that are in the map. First posted on arXiv 8 Mar 2022.

Builds on

16 of 25 references.

Closest ancestors · 1

Same direction, newest first

  1. 2021Score-based diffusion models for accelerated MRI8 Oct

Other references · 15

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  3. 2020Denoising Diffusion Implicit Modelscited by 9,655
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  6. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
  7. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 340
  8. 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 231
  9. 2021On Fast Sampling of Diffusion Probabilistic Modelscited by 170
  10. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
  11. 2021Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learningcited by 78
  12. 2020DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Priorcited by 72
3 more
  1. 2020KiU-Net: Towards Accurate Segmentation of Biomedical Images using Over-complete Representationscited by 49
  2. 2019GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstructioncited by 45
  3. 2021Over-and-Under Complete Convolutional RNN for MRI Reconstructioncited by 29

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 19; 2024: 24; 2025: 9; 2026: 3.

59 papers.

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  1. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  2. 2022Diffusion Models for Medical Image Analysis: A Comprehensive Surveycited by 269
  3. 2022GENIE: Higher-Order Denoising Diffusion Solverscited by 127
  4. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstructioncited by 117
  5. 2024MAISI: Medical AI for Synthetic Imagingcited by 86
  6. 2024Diffusion Models in Low-Level Vision: A Surveycited by 73
  7. 2023On the Design Fundamentals of Diffusion Models: A Surveycited by 59
  8. 2023Self-Supervised MRI Reconstruction with Unrolled Diffusion Modelscited by 37
  9. 2024Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolutioncited by 29
  10. 2024Self-Consistent Recursive Diffusion Bridge for Medical Image Translationcited by 26
  11. 2023Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Modelscited by 22
  12. 2023Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoisingcited by 21
38 more
  1. 2023Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstructioncited by 20
  2. 2024DPER: Diffusion Prior Driven Neural Representation for Limited Angle and Sparse View CT Reconstructioncited by 17
  3. 2023Learning Fourier-Constrained Diffusion Bridges for MRI Reconstructioncited by 13
  4. 2025Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Modelcited by 13
  5. 2023SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRIcited by 12
  6. 2025Metrics that matter: Evaluating image quality metrics for medical image generationcited by 12
  7. 2023CDiffMR: Can We Replace the Gaussian Noise with K-Space Undersampling for Fast MRI?cited by 11
  8. 2023DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embeddingcited by 10
  9. 2023Uncertainty Estimation and Propagation in Accelerated MRI Reconstructioncited by 10
  10. 2023DiffuseIR:Diffusion Models For Isotropic Reconstruction of 3D Microscopic Imagescited by 9
  11. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  12. 2023Fast Controllable Diffusion Models for Undersampled MRI Reconstructioncited by 8
  13. 2024TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet methodcited by 5
  14. 2024Stability and Generalizability in SDE Diffusion Models with Measure-Preserving Dynamicscited by 5
  15. 2024Highly Accelerated MRI via Implicit Neural Representation Guided Posterior Sampling of Diffusion Modelscited by 5
  16. 2023Robust Physics-based Deep MRI Reconstruction Via Diffusion Purificationcited by 4
  17. 2023Robust semi-supervised segmentation with timestep ensembling diffusion modelscited by 4
  18. 2024RadGazeGen: Radiomics and Gaze-guided Medical Image Generation using Diffusion Modelscited by 4
  19. 2024MRPD: Undersampled MRI reconstruction by prompting a large latent diffusion modelcited by 3
  20. 2024Score-based Generative Priors Guided Model-driven Network for MRI Reconstructioncited by 3
  21. 2024Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprintingcited by 3
  22. 2025DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI Reconstructioncited by 3
  23. 2025Reconstruct or Generate: Exploring the Spectrum of Generative Modeling for Cardiac MRIcited by 3
  24. 2023Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancementcited by 2
  25. 2023Constrained Probabilistic Mask Learning for Task-specific Undersampled MRI Reconstructioncited by 2
  26. 2024A Flow-based Truncated Denoising Diffusion Model for Super-resolution Magnetic Resonance Spectroscopic Imagingcited by 2
  27. 2024Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstructioncited by 2
  28. 2025Physics informed guided diffusion for accelerated multi-parametric MRI reconstructioncited by 2
  29. 2023Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representationscited by 1
  30. 2024DuDoUniNeXt: Dual-domain unified hybrid model for single and multi-contrast undersampled MRI reconstructioncited by 1
  31. 2024Clinically Feasible Diffusion Reconstruction for Highly-Accelerated Cardiac Cine MRIcited by 1
  32. 2024Spatiotemporal Diffusion Model with Paired Sampling for Accelerated Cardiac Cine MRIcited by 1
  33. 2024Structural and Convergence Analysis of Discrete-Time Denoising Diffusion Probabilistic Modelscited by 1
  34. 2024Guided MRI Reconstruction via Schr"odinger Bridgecited by 1
  35. 2024Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstructioncited by 1
  36. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1
  37. 2025Computationally Efficient Diffusion Models in Medical Imaging: A Comprehensive Reviewcited by 1
  38. 2025USB: Unified Synthetic Brain Framework for Bidirectional Pathology-Healthy Generation and Editingcited by 1

50 most cited of 59 All 59 (JSON)

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  1. 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
  2. 2021Come-Closer-Diffuse-Faster: Accelerating Conditional Diffusion Models for Inverse Problems through Stochastic Contraction
  3. 2023Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
  4. 2025Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models
  5. 2022High-Frequency Space Diffusion Models for Accelerated MRI
  6. 2022Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models
  7. 2024Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Data
  8. 2024Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation