Manifold-Aware Swin UNET Transformer for High-Fidelity Diffusion Tensor Imaging from Six Directions

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.