标题： MDPG：多领域扩散先验引导的MRI重建 显示英文标题

标题： MDPG: Multi-domain Diffusion Prior Guidance for MRI Reconstruction

摘要： 磁共振成像（MRI）重建在医学诊断中至关重要。 由于图像域中的随机性，最新的生成模型扩散模型（DMs）难以生成高保真图像。 潜在扩散模型（LDMs）在潜在域中产生紧凑且详细的先验知识，这可以有效地引导模型学习原始数据分布。 受此启发，我们提出由预训练LDMs提供的多域扩散先验指导（MDPG），以增强MRI重建任务中的数据一致性。 具体来说，我们首先构建一个基于Visual-Mamba的主干网络，该网络能够高效地对欠采样图像进行编码和重建。 然后，预训练的LDMs被集成以在潜在域和图像域中提供条件先验。 提出了一种新颖的潜在引导注意机制（LGA），用于多级潜在域中的高效融合。 同时，为了有效利用k空间和图像域中的先验信息，欠采样图像通过双域融合分支（DFB）与生成的全采样图像进行融合，以实现自适应指导。 最后，为进一步增强数据一致性，我们提出了一种基于非自校准信号（NACS）集的k空间正则化策略。 在两个公开的MRI数据集上的大量实验充分证明了所提方法的有效性。 代码可在 https://github.com/Zolento/MDPG 获取。 显示英文摘要

摘要： Magnetic Resonance Imaging (MRI) reconstruction is essential in medical diagnostics. As the latest generative models, diffusion models (DMs) have struggled to produce high-fidelity images due to their stochastic nature in image domains. Latent diffusion models (LDMs) yield both compact and detailed prior knowledge in latent domains, which could effectively guide the model towards more effective learning of the original data distribution. Inspired by this, we propose Multi-domain Diffusion Prior Guidance (MDPG) provided by pre-trained LDMs to enhance data consistency in MRI reconstruction tasks. Specifically, we first construct a Visual-Mamba-based backbone, which enables efficient encoding and reconstruction of under-sampled images. Then pre-trained LDMs are integrated to provide conditional priors in both latent and image domains. A novel Latent Guided Attention (LGA) is proposed for efficient fusion in multi-level latent domains. Simultaneously, to effectively utilize a prior in both the k-space and image domain, under-sampled images are fused with generated full-sampled images by the Dual-domain Fusion Branch (DFB) for self-adaption guidance. Lastly, to further enhance the data consistency, we propose a k-space regularization strategy based on the non-auto-calibration signal (NACS) set. Extensive experiments on two public MRI datasets fully demonstrate the effectiveness of the proposed methodology. The code is available at https://github.com/Zolento/MDPG.