Diff-DTI: Fast Diffusion Tensor Imaging Using A Feature-Enhanced Joint Diffusion Model

TL;DR

Diff-DTI is a novel model that accelerates diffusion tensor imaging by accurately generating DTI maps from fewer diffusion-weighted images, using a joint diffusion approach and feature enhancement to preserve details.

Contribution

It introduces a feature-enhanced joint diffusion model that enables high-quality DTI map reconstruction from limited data, reducing scan time significantly.

Findings

Outperforms existing methods in visual quality and metrics

Achieves high-fidelity DTI maps with only three DWIs

Overcomes the six-DWI minimum requirement

Abstract

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.