mlsanityData to 14 Oct 2026

Physics informed guided diffusion for accelerated multi-parametric MRI reconstruction

arXiv 2506.23311 ↗First posted 29 Jun 2025eess.IV cs.LG physics.med-phcited by 2

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

We introduce MRF-DiPh, a novel physics informed denoising diffusion approach for multiparametric tissue mapping from highly accelerated, transient-state quantitative MRI acquisitions like Magnetic Resonance Fingerprinting (MRF). Our method is derived from a proximal splitting formulation, incorporating a pretrained denoising diffusion model as an effective image prior to regularize the MRF inverse problem. Further, during reconstruction it simultaneously enforces two key physical constraints: (1) k-space measurement consistency and (2) adherence to the Bloch response model. Numerical experiments on in-vivo brain scans data show that MRF-DiPh outperforms deep learning and compressed sensing MRF baselines, providing more accurate parameter maps while better preserving measurement fidelity and physical model consistency-critical for solving reliably inverse problems in medical imaging.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 20 of its 35 references that are in the map. First posted on arXiv 29 Jun 2025.

Builds on

20 of 35 references.

Closest ancestors · 6

Same direction, newest first

  1. 2024Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprinting29 Oct
  2. 2023Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction2 Sep
  3. 2023Self-Supervised MRI Reconstruction with Unrolled Diffusion Models29 Jun
  4. 2022DOLCE: A Model-Based Probabilistic Diffusion Framework for Limited-Angle CT Reconstruction22 Nov
  5. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstruction12 Jul
  6. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model sampling8 Mar

Other references · 14

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  6. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,416
  7. 2020Plug-and-Play Image Restoration with Deep Denoiser Priorcited by 470
  8. 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 340
  9. 2021Robust Compressed Sensing MRI with Deep Generative Priorscited by 286
  10. 2020Rapid three-dimensional multiparametric MRI with quantitative transient-state imagingcited by 5
  11. 2022A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoiserscited by 5
  12. 2018Magnetic Resonance Fingerprinting Reconstruction via Spatiotemporal Convolutional Neural Networkscited by 4
2 more
  1. 2021Optimized multi-axis spiral projection MR fingerprinting with subspace reconstruction for rapid whole-brain high-isotropic-resolution quantitative imagingcited by 4
  2. 2018CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstructioncited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025q3-MuPa: Quick, Quiet, Quantitative Multi-Parametric MRI using Physics-Informed Diffusion Modelscited by 1
  2. 2026MRI2Qmap: multi-parametric quantitative mapping with MRI-driven denoising priorscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
  2. 2025Physics-Guided Diffusion Priors for Multi-Slice Reconstruction in Scientific Imaging
  3. 2023Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
  4. 2022Physics-informed Deep Diffusion MRI Reconstruction with Synthetic Data: Break Training Data Bottleneck in Artificial Intelligence
  5. 2022High-Frequency Space Diffusion Models for Accelerated MRI
  6. 2023Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction
  7. 2025Highly Undersampled MRI Reconstruction via a Single Posterior Sampling of Diffusion Models
  8. 2024Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction