用3D扩散模型+正则化，提升低采样率下的3D MRI重建质量。
MRI Reconstruction with Regularized 3D Diffusion Model (R3DM)
- 基于3D扩散模型结合优化方法，引入先验信息增强重建。
- 在临床与植物科学数据集上，显著降低噪声并提升图像保真度。
- 适用于低采样率、分布外数据，适合医学与生物成像领域。
磁共振成像（MRI）是一种广泛用于人体结构及植物科学等领域成像的强大技术。然而，为从欠采样k空间数据中快速重建出物体的精细结构，亟需高效算法。现有方法多局限于2D处理，本文提出一种基于正则化3D扩散模型的3D MRI重建方法，融合扩散先验以提升图像质量、抑制噪声并增强整体保真度。我们在临床与植物科学的MRI数据集上进行系统实验，评估了该方法在多种欠采样模式下，以及使用分布内/分布外预训练数据时的重建性能。结果表明，该方法优于对比的现有方法。
原文摘要 · Abstract (English)
Magnetic Resonance Imaging (MRI) is a powerful imaging technique widely used for visualizing structures within the human body and in other fields such as plant sciences. However, there is a demand to develop fast 3D-MRI reconstruction algorithms to show the fine structure of objects from under-sampled acquisition data, i.e., k-space data. This emphasizes the need for efficient solutions that can handle limited input while maintaining high-quality imaging. In contrast to previous methods only using 2D, we propose a 3D MRI reconstruction method that leverages a regularized 3D diffusion model combined with optimization method. By incorporating diffusion based priors, our method improves image quality, reduces noise, and enhances the overall fidelity of 3D MRI reconstructions. We conduct comprehensive experiments analysis on clinical and plant science MRI datasets. To evaluate the algorithm effectiveness for under-sampled k-space data, we also demonstrate its reconstruction performance with several undersampling patterns, as well as with in- and out-of-distribution pre-trained data. In experiments, we show that our method improves upon tested competitors.
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