Learning Spatially-Continuous Fiber Orientation Functions
Tyler Spears P. Thomas Fletcher
Our understanding of the human connectome is fundamentally limited by the
resolution of diffusion MR images. Reconstructing a connectome's constituent
neural pathways with tractography requires following a continuous field of
fiber directions. Typically, this field is found with simple trilinear
interpolation in low-resolution, noisy diffusion MRIs. However, trilinear
interpolation struggles following fine-scale changes in low-quality data.
Recent deep learning methods in super-resolving diffusion MRIs have focused on
upsampling to a fixed spatial grid, but this does not satisfy tractography's
need for a continuous field. In this work, we propose FENRI, a novel method
that learns spatially-continuous fiber orientation density functions from
low-resolution diffusion-weighted images. To quantify FENRI's capabilities in
tractography, we also introduce an expanded simulated dataset built for
evaluating deep-learning tractography models. We demonstrate that FENRI
accurately predicts high-resolution fiber orientations from realistic
low-quality data, and that FENRI-based tractography offers improved streamline
reconstruction over the current use of trilinear interpolation.