  Connectivity information derived from diffusion-weighted magnetic resonance
images~(DW-MRIs) plays an important role in studying human subcortical gray
matter structures. However, due to the $O(N^2)$ complexity of computing the
connectivity of each voxel to every other voxel (or multiple ROIs), the current
practice of extracting connectivity information is highly inefficient. This
makes the processing of high-resolution images and population-level analyses
very computationally demanding. To address this issue, we propose a more
efficient way to extract connectivity information; briefly, we consider two
regions/voxels to be connected if a white matter fiber streamline passes
through them -- no matter where the streamline originates. We consider the
thalamus parcellation task for demonstration purposes; our experiments show
that our approach brings a 30 to 120 times speedup over traditional approaches
with comparable qualitative parcellation results. We also demonstrate
high-resolution connectivity features can be super-resolved from low-resolution
DW-MRI in our framework. Together, these two innovations enable higher
resolution connectivity analysis from DW-MRI. Our source code is availible at
jasonbian97.github.io/fastcod.
