Diffusion MRI (dMRI) is an important neuroimaging technique with high
acquisition costs. Deep learning approaches have been used to enhance dMRI and
predict diffusion biomarkers through undersampled dMRI. To generate more
comprehensive raw dMRI, generative adversarial network based methods are
proposed to include b-values and b-vectors as conditions, but they are limited
by unstable training and less desirable diversity. The emerging diffusion model
(DM) promises to improve generative performance. However, it remains
challenging to include essential information in conditioning DM for more
relevant generation, i.e., the physical principles of dMRI and white matter
tract structures. In this study, we propose a physics-guided diffusion model to
generate high-quality dMRI. Our model introduces the physical principles of
dMRI in the noise evolution in the diffusion process and introduce a
query-based conditional mapping within the difussion model. In addition, to
enhance the anatomical fine detials of the generation, we introduce the XTRACT
atlas as prior of white matter tracts by adopting an adapter technique. Our
experiment results show that our method outperforms other state-of-the-art
methods and has the potential to advance dMRI enhancement.

本研究提出了一种基于物理原理的扩散模型来生成高质量的扩散磁共振成像，引入了扩散过程中的噪声演化物理原理和基于查询的条件映射，并通过采用适配器技术引入 XTRACT 图谱作为白质束的先验知识，实验结果表明我们的方法优于其他最先进的方法，并具有推进扩散磁共振成像增强的潜力。