Scaling global tractography with ExaTract: from large cohorts to ultra-high-resolution datasets

Abstract

Global tractography offers a theoretical framework for reconstructing white matter connectivity from diffusion MRI by jointly inferring all streamlines and their consistency with the diffusion signal and underlying anatomical priors. However, its sequential nature and log-linear complexity make it computationally prohibitive at the scales demanded by neuroimaging, from large population cohorts such as the Human Connectome Project (HCP) to ex vivo ultra-high-field acquisitions at mesoscopic resolution. In this work, we present ExaTract, a parallelized spin-glass tractography framework designed for high-performance computing (HPC) architectures. Implemented as an open-source MPI+Kokkos application, ExaTract partitions the tractography domain into spatially overlapping regions processed across distributed memory nodes, with regular synchronization of boundary modifications to preserve global convergence guarantees. We validate ExaTract on both in vivo and ex vivo diffusion MRI data: the complete HCP dataset demonstrates population-scale throughput, while the high-resolution Chenonceau dataset provides the first mesoscopic structural connectome derived from global tractography.