mlsanityData to 14 Oct 2026

Measurement Score-Based MRI Reconstruction with Automatic Coil Sensitivity Estimation

arXiv 2509.18402 ↗First posted 22 Sep 2025eess.IV cs.LGcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Diffusion-based inverse problem solvers (DIS) have recently shown outstanding performance in compressed-sensing parallel MRI reconstruction by combining diffusion priors with physical measurement models. However, they typically rely on pre-calibrated coil sensitivity maps (CSMs) and ground truth images, making them often impractical: CSMs are difficult to estimate accurately under heavy undersampling and ground-truth images are often unavailable. We propose Calibration-free Measurement Score-based diffusion Model (C-MSM), a new method that eliminates these dependencies by jointly performing automatic CSM estimation and self-supervised learning of measurement scores directly from k-space data. C-MSM reconstructs images by approximating the full posterior distribution through stochastic sampling over partial measurement posterior scores, while simultaneously estimating CSMs. Experiments on the multi-coil brain fastMRI dataset show that C-MSM achieves reconstruction performance close to DIS with clean diffusion priors -- even without access to clean training data and pre-calibrated CSMs.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 9 of its 13 references that are in the map. First posted on arXiv 22 Sep 2025.

Builds on

9 of 13 references.

References · 9

  1. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  2. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,416
  3. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Modelcited by 583
  4. 2018fastMRI: An Open Dataset and Benchmarks for Accelerated MRIcited by 470
  5. 2024A Survey on Diffusion Models for Inverse Problemscited by 180
  6. 2023Ambient Diffusion: Learning Clean Distributions from Corrupted Datacited by 111
  7. 2024Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models Trained on Corrupted Datacited by 32
  8. 2022SPICER: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation and Reconstructioncited by 10
  9. 2025Measurement Score-Based Diffusion Modelcited by 8

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  1. 2022Self-Score: Self-Supervised Learning on Score-Based Models for MRI Reconstruction
  2. 2021Score-based diffusion models for accelerated MRI
  3. 2023Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
  4. 2025Patch-Based Diffusion for Data-Efficient, Radiologist-Preferred MRI Reconstruction
  5. 2025Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
  6. 2023Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction
  7. 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
  8. 2022Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction