Keywords: Analysis/Processing, Brain, AI/ML Image Reconstruction ,Diffusion Analysis & Visualization, DTI
Motivation: Diffusion Tensor Imaging (DTI) requires numerous diffusion weighted images, resulting in long scan sessions that motivate a need for more efficient DTI estimation techniques.
Goal(s): Our goal is to demonstrate that Deep Neural Networks (DNNs) trained with a manifold-respecting loss function can more accurately estimate diffusion tensors from fewer diffusion-weighted images, surpassing networks trained with Euclidean losses while honoring the tensors' manifold structure.
Approach: We employed the Swin UNET Transformer architecture and trained two models: one with Log-Euclidean loss and another with Euclidean loss.
Results: When evaluating the predicted tensors against conventional techniques, our approach consistently outshined the rest.
Impact: The study will enable an accurate estimate of brain microstructure from DTI data acquired with six gradient directions by developing manifold-aware DNN for DTI analysis. This breakthrough may reduce patient discomfort and scanning artifacts, and potentially increase imaging centers throughput.
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