Bian Wanyu, Jang Albert, Zhang Liping, Yang Xiaonan, Stewart Zachary, Liu Fang
IEEE Trans Med Imaging. 2024 Aug 8;PP. doi: 10.1109/TMI.2024.3440227.
This study introduces a novel image reconstruction technique based on a diffusion model that is conditioned on the native data domain. Our method is applied to multi-coil MRI and quantitative MRI (qMRI) 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 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.
本研究介绍了一种基于扩散模型的新型图像重建技术，该模型以原始数据域为条件。我们的方法应用于多线圈磁共振成像（MRI）和定量MRI（qMRI）重建，在频率域和参数域利用域条件扩散模型。将先验MRI物理知识用作扩散模型中的嵌入，强制数据一致性以指导训练和采样过程，表征MRI重建中的MRI k空间编码，并利用MR信号建模进行qMRI重建。此外，在扩散步骤中纳入梯度下降优化，增强特征学习并改善去噪。所提出的方法显示出巨大的前景，特别是对于以高加速因子重建图像。值得注意的是，它在各种解剖结构的静态和定量MRI重建中保持了很高的重建精度。除了其直接应用外，该方法还具有潜在的泛化能力，使其适用于跨各种领域的逆问题。
