  Magnetic resonance diffusion tensor imaging (DTI) is a critical tool for
neural disease diagnosis. However, long scan time greatly hinders the
widespread clinical use of DTI. To accelerate image acquisition, a
feature-enhanced joint diffusion model (Diff-DTI) is proposed to obtain
accurate DTI parameter maps from a limited number of diffusion-weighted images
(DWIs). Diff-DTI introduces a joint diffusion model that directly learns the
joint probability distribution of DWIs with DTI parametric maps for conditional
generation. Additionally, a feature enhancement fusion mechanism (FEFM) is
designed and incorporated into the generative process of Diff-DTI to preserve
fine structures in the generated DTI maps. A comprehensive evaluation of the
performance of Diff-DTI was conducted on the Human Connectome Project dataset.
The results demonstrate that Diff-DTI outperforms existing state-of-the-art
fast DTI imaging methods in terms of visual quality and quantitative metrics.
Furthermore, Diff-DTI has shown the ability to produce high-fidelity DTI maps
with only three DWIs, thus overcoming the requirement of a minimum of six DWIs
for DTI.
