A mesoscale ex vivo macaque white matter atlas using high-performance computing global tractography

COEX 

While global tractography offers enhanced accuracy and reliability compared to standard streamline tractography methods [1], it comes at the cost of substantial computational resources in terms of both time and memory. This approach simultaneously generates and optimizes the trajectories of virtual axonal white matter (WM) fibers (represented as connected spin-glasses), considering the local orientation distribution derived from diffusion MRI (dMRI) data. Leveraging this method on extensive datasets poses significant challenges, leading to the creation of ExaTract [2], a novel High-Performance Computing (HPC) global tractography approach.
In this study, we demonstrate the ability of ExaTract to reconstruct numerous brain connections and robustly identify deep WM bundles from a very high resolution (250μm) dataset in a reasonable time.