mlsanityData to 7 Oct 2026

Noisy MRI Reconstruction via MAP Estimation with an Implicit Deep-Denoiser Prior

arXiv 2511.11963 ↗First posted 15 Nov 2025eess.IVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Accelerating magnetic resonance imaging (MRI) remains challenging, particularly under realistic acquisition noise. While diffusion models have recently shown promise for reconstructing undersampled MRI data, many approaches lack an explicit link to the underlying MRI physics, and their parameters are sensitive to measurement noise, limiting their reliability in practice. We introduce Implicit-MAP (ImMAP), a diffusion-based reconstruction framework that integrates the acquisition noise model directly into a maximum a posteriori (MAP) formulation. Specifically, we build on the stochastic ascent method of Kadkhodaie et al. and generalize it to handle MRI encoding operators and realistic measurement noise. Across both simulated and real noisy datasets, ImMAP consistently outperforms state-of-the-art deep learning (LPDSNet) and diffusion-based (DDS) methods. By clarifying the practical behavior and limitations of diffusion models under realistic noise conditions, ImMAP establishes a more reliable and interpretable

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 12 of its 16 references that are in the map. First posted on arXiv 15 Nov 2025.

Builds on

12 of 16 references.

References · 12

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  3. 2023Denoising Diffusion Models for Plug-and-Play Image Restorationcited by 335
  4. 2020End-to-End Variational Networks for Accelerated MRI Reconstructioncited by 201
  5. 2024A Survey on Diffusion Models for Inverse Problemscited by 173
  6. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problemscited by 129
  7. 2024Learning Diffusion Priors from Observations by Expectation Maximizationcited by 60
  8. 2021CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicingcited by 2
  9. 2024GroupCDL: Interpretable Denoising and Compressed Sensing MRI via Learned Group-Sparsity and Circulant Attentioncited by 2
  10. 2025Self-Supervised Noise Adaptive MRI Denoising via Repetition to Repetition (Rep2Rep) Learningcited by 2
  11. 2022Gabor is Enough: Interpretable Deep Denoising with a Gabor Synthesis Dictionary Priorcited by 1
  12. 2025Learned Primal Dual Splitting for Self-Supervised Noise-Adaptive MRI Reconstructioncited by 1

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  1. 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
  2. 2023DDM2: Self-Supervised Diffusion MRI Denoising with Generative Diffusion Models
  3. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstruction
  4. 2024Resolution-Robust 3D MRI Reconstruction with 2D Diffusion Priors: Diverse-Resolution Training Outperforms Interpolation
  5. 2024Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction
  6. 2026Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models
  7. 2026High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction
  8. 2024On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstruction