mlsanityData to 7 Oct 2026

Bayesian MRI Reconstruction with Joint Uncertainty Estimation using Diffusion Models

arXiv 2202.01479 ↗First posted 3 Feb 2022cs.LGcited by 46

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

We introduce a framework that enables efficient sampling from learned probability distributions for MRI reconstruction. Different from conventional deep learning-based MRI reconstruction techniques, samples are drawn from the posterior distribution given the measured k-space using the Markov chain Monte Carlo (MCMC) method. In addition to the maximum a posteriori (MAP) estimate for the image, which can be obtained with conventional methods, the minimum mean square error (MMSE) estimate and uncertainty maps can also be computed. The data-driven Markov chains are constructed from the generative model learned from a given image database and are independent of the forward operator that is used to model the k-space measurement. This provides flexibility because the method can be applied to k-space acquired with different sampling schemes or receive coils using the same pre-trained models. Furthermore, we use a framework based on a reverse diffusion process to be able to utilize advanced generative models. The performance of the method is evaluated on an open dataset using 10-fold undersampling in k-space.

Key facts. Cited by 46 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 17 of its 49 references that are in the map. First posted on arXiv 3 Feb 2022.

Builds on

17 of 49 references.

Closest ancestors · 1

Same direction, newest first

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

Other references · 16

  1. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  3. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  4. 2013Auto-Encoding Variational Bayescited by 3,980
  5. 2017Non-local Neural Networkscited by 2,290
  6. 2017Arbitrary Style Transfer in Real-time with Adaptive Instance Normalizationcited by 1,998
  7. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 464
  8. 2016RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentationcited by 463
  9. 2017PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modificationscited by 415
  10. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 384
  11. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 340
  12. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 284
4 more
  1. 2017MR image reconstruction using deep density priorscited by 37
  2. 2021Bayesian Uncertainty Estimation of Learned Variational MRI Reconstructioncited by 30
  3. 2019MRI Reconstruction Using Deep Bayesian Estimationcited by 28
  4. 2020XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challengecited by 13

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 14; 2024: 12; 2025: 10; 2026: 8.

46 papers.

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  1. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problemscited by 129
  2. 2025InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciencescited by 67
  3. 2023Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Datacited by 40
  4. 2023A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methodscited by 39
  5. 2024On the Challenges and Opportunities in Generative AIcited by 31
  6. 2022Stable Deep MRI Reconstruction using Generative Priorscited by 21
  7. 2023The Emergence of Reproducibility and Generalizability in Diffusion Modelscited by 21
  8. 2023Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstructioncited by 20
  9. 2023MRI Reconstruction with Side Information using Diffusion Modelscited by 15
  10. 2023Correlated and Multi-frequency Diffusion Modeling for Highly Under-sampled MRI Reconstructioncited by 14
  11. 2023Learning Fourier-Constrained Diffusion Bridges for MRI Reconstructioncited by 13
  12. 2022One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstructioncited by 11
34 more
  1. 2023A Survey of Emerging Applications of Diffusion Probabilistic Models in MRIcited by 9
  2. 2024Self-Supervised Adversarial Diffusion Models for Fast MRI Reconstructioncited by 9
  3. 2024Noise Level Adaptive Diffusion Model for Robust Reconstruction of Accelerated MRIcited by 6
  4. 2025Diffusion at Absolute Zero: Langevin Sampling Using Successive Moreau Envelopes [conference paper]cited by 6
  5. 2024Highly Accelerated MRI via Implicit Neural Representation Guided Posterior Sampling of Diffusion Modelscited by 5
  6. 2023Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODEcited by 4
  7. 2024Autoregressive Image Diffusion: Generation of Image Sequence and Application in MRIcited by 4
  8. 2024Robust multi-coil MRI reconstruction via self-supervised denoisingcited by 4
  9. 2025Antithetic Noise in Diffusion Modelscited by 4
  10. 2025Self-diffusion for Solving Inverse Problemscited by 4
  11. 2023PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classificationcited by 3
  12. 2023Generative Priors for MRI Reconstruction Trained from Magnitude-Only Images Using Phase Augmentationcited by 3
  13. 2024INFusion: Diffusion Regularized Implicit Neural Representations for 2D and 3D accelerated MRI reconstructioncited by 3
  14. 2024Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprintingcited by 3
  15. 2023Joint Non-Linear MRI Inversion with Diffusion Priorscited by 2
  16. 2024Robust Simultaneous Multislice MRI Reconstruction Using Slice-Wise Learned Generative Diffusion Priorscited by 2
  17. 2024Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstructioncited by 2
  18. 2024Fast MRI for All: Bridging Access Gaps by Training without Raw Datacited by 2
  19. 2025Unsupervised Radar Point Cloud Enhancement via Arbitrary LiDAR Guided Diffusion Priorcited by 2
  20. 2025How many measurements are enough? Bayesian recovery in inverse problems with general distributionscited by 2
  21. 2025Guiding Quantitative MRI Reconstruction with Phase-wise Uncertaintycited by 1
  22. 2025A Comprehensive Survey on Magnetic Resonance Image Reconstructioncited by 1
  23. 2025Diffusion Probabilistic Generative Models for Accelerated, in-NICU Permanent Magnet Neonatal MRIcited by 1
  24. 2026MUST: Modality-Specific Representation-Aware Transformer for Diffusion-Enhanced Survival Prediction with Missing Modalitycited by 1
  25. 2023Bias-Reduced Neural Networks for Parameter Estimation in Quantitative MRIcited by 0
  26. 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
  27. 2025Fast and Robust Diffusion Posterior Sampling for MR Image Reconstruction Using the Preconditioned Unadjusted Langevin Algorithmcited by 0
  28. 2026Beyond Accuracy: Evaluating Posterior Fidelity of Diffusion Inverse Solverscited by 0
  29. 2026MRI2Qmap: multi-parametric quantitative mapping with MRI-driven denoising priorscited by 0
  30. 2026Accelerating Stroke MRI with Diffusion Probabilistic Models through Large-Scale Pre-training and Target-Specific Fine-Tuningcited by 0
  31. 2026CARE: Training-Free Controllable Restoration for Medical Images via Dual-Latent Steeringcited by 0
  32. 2026Product-of-Gaussian-Mixture Diffusion Models for Joint Nonlinear MRI Reconstructioncited by 0
  33. 2026Consistency Models for Fast MRI Reconstruction Using Regularization by Denoisingcited by 0
  34. 2026Uncertainty-Aware 3D Residual Wavelet Diffusion for Ultra Low-Field MRI Super-Resolutioncited by 0

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  1. 2023Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
  2. 2026High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
  3. 2024Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors
  4. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction
  5. 2024Bayesian computation with generative diffusion models by Multilevel Monte Carlo
  6. 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
  7. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model sampling
  8. 2025Active MRI Acquisition with Diffusion Guided Bayesian Experimental Design