Keywords: AI Diffusion Models, Diffusion Tensor Imaging
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.
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