标题： 基于领域条件先验引导的扩散模型用于加速MRI和qMRI重建 显示英文标题

标题： Diffusion Modeling with Domain-conditioned Prior Guidance for Accelerated MRI and qMRI Reconstruction

摘要： 本研究提出了一种基于原生数据域条件的扩散模型的图像重建新方法。 我们的方法应用于多线圈MRI和定量MRI重建，利用频率域和参数域内的领域条件扩散模型。 先验MRI物理模型作为扩散模型中的嵌入，强制数据一致性以指导训练和采样过程，表征MRI k空间编码在MRI重建中的特性，并利用MR信号建模进行qMRI重建。 此外，将梯度下降优化引入扩散步骤中，增强了特征学习并提高了去噪效果。 所提出的方法表现出显著的前景，特别是在高加速因子下重建图像方面。 值得注意的是，它在各种解剖结构上的静态MRI和定量MRI重建中保持了出色的重建精度和效率。 除了直接应用外，该方法提供了潜在的泛化能力，使其能够适应跨多个领域的逆问题。 显示英文摘要

摘要： This study introduces a novel approach for image reconstruction based on a diffusion model conditioned on the native data domain. Our method is applied to multi-coil MRI and quantitative MRI reconstruction, leveraging the domain-conditioned diffusion model within the frequency and parameter domains. The prior MRI physics are used as embeddings in the diffusion model, enforcing data consistency to guide the training and sampling process, characterizing MRI k-space encoding in MRI reconstruction, and leveraging MR signal modeling for qMRI reconstruction. Furthermore, a gradient descent optimization is incorporated into the diffusion steps, enhancing feature learning and improving denoising. The proposed method demonstrates a significant promise, particularly for reconstructing images at high acceleration factors. Notably, it maintains great reconstruction accuracy and efficiency for static and quantitative MRI reconstruction across diverse anatomical structures. Beyond its immediate applications, this method provides potential generalization capability, making it adaptable to inverse problems across various domains.