  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.
