Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data

• URL: http://arxiv.org/abs/2510.06335v1
• Date: Tue, 07 Oct 2025 18:01:08 GMT
• Title: Conditional Denoising Diffusion Model-Based Robust MR Image Reconstruction from Highly Undersampled Data
• Authors: Mohammed Alsubaie, Wenxi Liu, Linxia Gu, Ovidiu C. Andronesi, Sirani M. Perera, Xianqi Li,
• Abstract summary: Undersampling strategies can accelerate image acquisition, but they often result in image artifacts and degraded quality.<n>Recent diffusion models have shown promise for reconstructing high-fidelity images from undersampled data by learning powerful image priors.<n>We introduce a conditional denoising diffusion framework with iterative data-consistency correction.
• Score: 11.174208209806073
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Magnetic Resonance Imaging (MRI) is a critical tool in modern medical diagnostics, yet its prolonged acquisition time remains a critical limitation, especially in time-sensitive clinical scenarios. While undersampling strategies can accelerate image acquisition, they often result in image artifacts and degraded quality. Recent diffusion models have shown promise for reconstructing high-fidelity images from undersampled data by learning powerful image priors; however, most existing approaches either (i) rely on unsupervised score functions without paired supervision or (ii) apply data consistency only as a post-processing step. In this work, we introduce a conditional denoising diffusion framework with iterative data-consistency correction, which differs from prior methods by embedding the measurement model directly into every reverse diffusion step and training the model on paired undersampled-ground truth data. This hybrid design bridges generative flexibility with explicit enforcement of MRI physics. Experiments on the fastMRI dataset demonstrate that our framework consistently outperforms recent state-of-the-art deep learning and diffusion-based methods in SSIM, PSNR, and LPIPS, with LPIPS capturing perceptual improvements more faithfully. These results demonstrate that integrating conditional supervision with iterative consistency updates yields substantial improvements in both pixel-level fidelity and perceptual realism, establishing a principled and practical advance toward robust, accelerated MRI reconstruction.

Related papers

• Implicit Neural Representation-Based Continuous Single Image Super Resolution: An Empirical Study [50.15623093332659]
  Implicit neural representation (INR) has become the standard approach for arbitrary-scale image super-resolution (ASSR)<n>We compare existing techniques across diverse settings and present aggregated performance results on multiple image quality metrics.<n>We examine a new loss function that penalizes intensity variations while preserving edges, textures, and finer details during training.
  arXiv  Detail & Related papers  (2026-01-25T07:09:20Z)
• Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model [105.95160543743984]
  We propose a deep learning procedure called Dual-Energy Decomposition Model-based Diffusion (DEcomp-MoD) for quantitative material decomposition.<n>We show that DEcomp-MoD outperform state-of-the-art unsupervised score-based model and supervised deep learning networks.
  arXiv  Detail & Related papers  (2025-07-24T01:00:06Z)
• ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning [51.26601171361753]
  We propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process.<n>We show that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-08T05:15:43Z)
• Re-Visible Dual-Domain Self-Supervised Deep Unfolding Network for MRI Reconstruction [48.30341580103962]
  We propose a novel re-visible dual-domain self-supervised deep unfolding network to address these issues.<n>We design a deep unfolding network based on Chambolle and Pock Proximal Point Algorithm (DUN-CP-PPA) to achieve end-to-end reconstruction.<n> Experiments conducted on the fastMRI and IXI datasets demonstrate that our method significantly outperforms state-of-the-art approaches in terms of reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-07T12:29:32Z)
• Self-Consistent Nested Diffusion Bridge for Accelerated MRI Reconstruction [22.589087990596887]
  We focus on the underexplored task of magnitude-image-based MRI reconstruction.<n>Recent advancements in diffusion models, particularly denoising diffusion probabilistic models, have demonstrated strong capabilities in modeling image priors.<n>We propose a novel Self-Consistent Nested Diffusion Bridge (SC-NDB) framework that models accelerated MRI reconstruction.
  arXiv  Detail & Related papers  (2024-12-13T09:35:34Z)
• Highly Accelerated MRI via Implicit Neural Representation Guided Posterior Sampling of Diffusion Models [2.5412006057370893]
  Implicit neural representation (INR) has emerged as a powerful paradigm for solving inverse problems. Our proposed framework can be a generalizable framework to solve inverse problems in other medical imaging tasks.
  arXiv  Detail & Related papers  (2024-07-03T01:37:56Z)
• Steerable Conditional Diffusion for Out-of-Distribution Adaptation in Medical Image Reconstruction [75.91471250967703]
  We introduce a novel sampling framework called Steerable Conditional Diffusion.<n>This framework adapts the diffusion model, concurrently with image reconstruction, based solely on the information provided by the available measurement.<n>We achieve substantial enhancements in out-of-distribution performance across diverse imaging modalities.
  arXiv  Detail & Related papers  (2023-08-28T08:47:06Z)
• Self-Supervised MRI Reconstruction with Unrolled Diffusion Models [27.143473617162304]
  We propose a novel self-supervised deep reconstruction model, named Self-Supervised Diffusion Reconstruction (SSDiffRecon) SSDiffRecon expresses a conditional diffusion process that interleaves cross-attention transformers for reverse diffusion steps with data-consistency blocks for physics-driven processing. Experiments on public brain MR datasets demonstrate the superiority of SSDiffRecon against state-of-the-art supervised, and self-supervised baselines in terms of reconstruction speed and quality.
  arXiv  Detail & Related papers  (2023-06-29T03:31:46Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• Iterative Data Refinement for Self-Supervised MR Image Reconstruction [18.02961646651716]
  We propose a data refinement framework for self-supervised MR image reconstruction. We first analyze the reason of the performance gap between self-supervised and supervised methods. Then, we design an effective self-supervised training data refinement method to reduce this data bias.
  arXiv  Detail & Related papers  (2022-11-24T06:57:16Z)
• Stable Deep MRI Reconstruction using Generative Priors [13.400444194036101]
  We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. The results demonstrate competitive performance, on par with state-of-the-art end-to-end deep learning methods.
  arXiv  Detail & Related papers  (2022-10-25T08:34:29Z)
• Adaptive Diffusion Priors for Accelerated MRI Reconstruction [0.9895793818721335]
  Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data. Unconditional models instead learn generative image priors decoupled from the operator to improve reliability against domain shifts related to the imaging operator. Here we propose the first adaptive diffusion prior for MRI reconstruction, AdaDiff, to improve performance and reliability against domain shifts.
  arXiv  Detail & Related papers  (2022-07-12T22:45:08Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.