mlsanityData to 7 Oct 2026

Sampling-Pattern-Agnostic MRI Reconstruction through Adaptive Consistency Enforcement with Diffusion Model

arXiv 2409.14479 ↗First posted 22 Sep 2024eess.IVcited by 0

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

Magnetic Resonance Imaging (MRI) is a powerful, non-invasive diagnostic tool; however, its clinical applicability is constrained by prolonged acquisition times. Whilst present deep learning-based approaches have demonstrated potential in expediting MRI processes, these methods usually rely on known sampling patterns and exhibit limited generalisability to novel patterns. In the paper, we propose a sampling-pattern-agnostic MRI reconstruction method via a diffusion model through adaptive consistency enforcement. Our approach effectively reconstructs high-fidelity images with varied under-sampled acquisitions, generalising across contrasts and acceleration factors regardless of sampling trajectories. We train and validate across all contrasts in the MICCAI 2024 Cardiac MRI Reconstruction Challenge (CMRxRecon) dataset for the ``Random sampling CMR reconstruction'' task. Evaluation results indicate that our proposed method significantly outperforms baseline methods.

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 20 of its 25 references that are in the map. First posted on arXiv 22 Sep 2024.

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20 of 25 references.

Closest ancestors · 3

Same direction, newest first

  1. 2023DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models8 Dec
  2. 2022Adaptive Diffusion Priors for Accelerated MRI Reconstruction12 Jul
  3. 2022Towards performant and reliable undersampled MR reconstruction via diffusion model sampling8 Mar

Other references · 17

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2020Denoising Diffusion Implicit Modelscited by 9,655
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  6. 2022Generative Adversarial Networkscited by 5,603
  7. 2019Generative Modeling by Estimating Gradients of the Data Distributioncited by 4,067
  8. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  9. 2022Diffusion Posterior Sampling for General Noisy Inverse Problemscited by 1,375
  10. 2022Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Modelcited by 574
  11. 2017MoDL: Model Based Deep Learning Architecture for Inverse Problemscited by 384
  12. 2017A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstructioncited by 231
5 more
  1. 2023Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Modelscited by 156
  2. 2023Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problemscited by 129
  3. 2017Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstructioncited by 105
  4. 2023Accelerating Diffusion Models for Inverse Problems through Shortcut Samplingcited by 21
  5. 2024CMRxRecon2024: A Multi-Modality, Multi-View K-Space Dataset Boosting Universal Machine Learning for Accelerated Cardiac MRIcited by 6

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Not cited yet.

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Close in wording, no citation either way

  1. 2025Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
  2. 2023Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
  3. 2023Robust Physics-based Deep MRI Reconstruction Via Diffusion Purification
  4. 2026Consistency Models for Fast MRI Reconstruction Using Regularization by Denoising
  5. 2024MRPD: Undersampled MRI reconstruction by prompting a large latent diffusion model
  6. 2026Frequency Error-Guided Under-sampling Optimization for Multi-Contrast MRI Reconstruction
  7. 2025Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining
  8. 2024On Instabilities of Unsupervised Denoising Diffusion Models in Magnetic Resonance Imaging Reconstruction