FastCod: Fast Brain Connectivity in Diffusion Imaging

TL;DR

FastCod introduces a highly efficient method for extracting brain connectivity from diffusion MRI data, significantly reducing computation time while maintaining quality, and enabling super-resolution analysis of high-resolution connectivity features.

Contribution

The paper presents a novel, faster approach to brain connectivity extraction from diffusion MRI that is 30 to 120 times more efficient and supports super-resolution connectivity analysis.

Findings

Achieves 30 to 120 times speedup over traditional methods.

Provides comparable qualitative parcellation results.

Enables super-resolution connectivity features from low-resolution data.

Abstract

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.