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

• Published in: bioRxiv
• Main Authors: Cai, Leon Y, Lee, Ho Hin, Newlin, Nancy R, Kerley, Cailey I, Kanakaraj, Praitayini, Yang, Qi, Johnson, Graham W, Moyer, Daniel, Schilling, Kurt G, Rheault, Fran Cois, Landman, Bennett A
• Format: Journal Article Paper
• Language: English
• Published: United States Cold Spring Harbor Laboratory Press 08.03.2023
• Subjects: Deep learning; Magnetic resonance imaging; Neural networks; Substantia alba; white matter; connectomics; tractography; T1-weighted MRI; bundles; diffusion MRI; convolutional-recurrent neural networks
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2023.02.25.530046

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. Specifically, we propose learning the streamline propagator from T1w MRI to facilitate arbitrary tractography analyses when dMRI is unavailable. To do so, we present a novel convolutional-recurrent neural network (CoRNN) trained in a teacher-student framework that leverages T1w MRI, associated anatomical context, and streamline memory from data acquired for the Human Connectome Project. We characterize our approach under two common tractography paradigms, WM bundle analysis and structural connectomics, and find approximately a 5-15% difference between measures computed from streamlines generated with our approach and those generated using traditional dMRI tractography. When placed in the literature, these results suggest that the accuracy of WM measures computed from T1w MRI with our method is on the level of scan-rescan dMRI variability and raise an important question: is tractography truly a microstructural phenomenon, or has dMRI merely facilitated its discovery and implementation?