arXiv 2510.063352025-10-07eess.IVcs.CVcs.LG

基于条件去噪扩散模型的高欠采样数据鲁棒磁共振图像重建

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

Department of Mathematics and Systems Engineering（数学与系统工程系）；Department of Biomedical Engineering & Science（生物医学工程与科学系）；Florida Institute of Technology（佛罗里达理工学院）；A. A. Martinos Center for Biomedical Imaging（A.A. Martinos生物医学成像中心）；Massachusetts General Hospital/Harvard Medical School（麻省总医院/哈佛医学院）；Department of Radiology（放射科）；Embry-Riddle Aeronautical University（艾姆伯-里尔航空大学）

Mohammed Alsubaie, Wenxi Liu, Linxia Gu, Ovidiu C. Andronesi, Sirani M. Perera, Xianqi Li

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AI总结：

本文提出一种将测量模型嵌入每个反向扩散步骤并迭代数据一致性校正的条件去噪扩散框架，在fastMRI数据集上优于现有方法，显著提升MRI重建的像素保真度与感知真实感。

摘要：

磁共振成像（MRI）是现代医学诊断中的关键工具，然而其较长的采集时间仍然是一个关键限制，尤其是在时间敏感的临床场景中。虽然欠采样策略可以加速图像采集，但通常会导致图像伪影和质量下降。最近的扩散模型通过学习强大的图像先验，在从欠采样数据重建高保真图像方面显示出潜力；然而，大多数现有方法要么（i）依赖无监督的评分函数而无需配对监督，要么（ii）仅将数据一致性作为后处理步骤应用。在这项工作中，我们引入了一种带有迭代数据一致性校正的条件去噪扩散框架，与先前方法不同，该框架将测量模型直接嵌入到每个反向扩散步骤中，并在配对的欠采样-真实数据上训练模型。这种混合设计弥合了生成灵活性与MRI物理显式约束之间的差距。在fastMRI数据集上的实验表明，我们的框架在SSIM、PSNR和LPIPS指标上持续优于最近最先进的深度学习和基于扩散的方法，其中LPIPS更忠实地捕捉感知改进。这些结果表明，将条件监督与迭代一致性更新相结合，在像素级保真度和感知真实感方面均产生了显著改进，为稳健、加速的MRI重建建立了一种原则性且实用的进展。

英文摘要：

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.

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