Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context.

Diffusion MRI (dMRI) streamline tractography is the gold-standard for estimation of white matter (WM) pathways in the brain. However, the high angular resolution dMRI acquisitions capable of fitting the microstructural models needed for tractography are often time-consuming and not routinely collected clinically, restricting the scope of tractography analyses. To address this limitation, we build on recent advances in deep learning which have demonstrated that streamline propagation can be learned from dMRI directly without traditional model fitting.