Accelerated Diffusion Tensor Imaging using A Diffusion Generative Deep Learning Model

Motivation: High-quality diffusion tensor imaging involves fitting a large number of diffusion-encoded images to a tensor model. This challenging process requires long scans and is vulnerable to motion artifacts. There’s a need for accelerated acquisitions while preserving robust diffusion tensor estimates. Goal(s): To develop a generative diffusion model that produces high-quality tensor metrics using a few diffusion-encoded images. Approach: The proposed generative model was trained using 300 randomly selected subjects from the Human Connectome Project Dataset and tested on 20 subjects. Results: Our model demonstrates the ability to generate high-quality tensor metrics for as few as 3 DWIs. Impact: This study demonstrates that a generative diffusion model can produce high-quality tensor metrics with significant reduction in scan time, potentially eliminating image distortions and artifacts.