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.

翻译：扩散磁共振成像（dMRI）是一种重要的神经影像技术，但其采集成本高昂。深度学习方法已被用于增强dMRI并通过欠采样dMRI预测扩散生物标志物。为生成更完整的原始dMRI数据，基于生成对抗网络的方法被提出，将b值和b向量作为条件输入，但这些方法受限于训练不稳定性和生成多样性不足。新兴的扩散模型有望提升生成性能，然而如何将关键信息有效融入条件扩散模型以生成更相关的数据——即dMRI的物理原理与白质纤维束结构——仍具挑战。本研究提出一种物理引导的扩散模型来生成高质量dMRI。该模型在扩散过程的噪声演化中引入dMRI物理原理，并在扩散模型内部构建基于查询的条件映射机制。此外，为增强生成结果的解剖细节，我们采用适配器技术引入XTRACT图谱作为白质纤维束先验知识。实验结果表明，本方法优于其他先进方法，并具备推动dMRI增强技术发展的潜力。