Implementation considerations for deep learning with diffusion MRI streamline tractography

Leon Yichen Cai, Ho Hin Lee, Nancy Rose Newlin, Michael Eugene Kim, Daniel Moyer, Francois Rheault, Kurt Gregory Schilling, Bennett A. Landman

Keywords: Diffusion MRI (dMRI) streamline tractography, deep learning, recurrent neural networks, PyTorch

TL;DR: We present implementation considerations for training recurrent neural networks for diffusion MRI tractography and release the associated source code.

Abstract: One area of medical imaging that has recently experienced innovative deep learning advances is diffusion MRI (dMRI) streamline tractography with recurrent neural networks (RNNs). Unlike traditional imaging studies which utilize voxel-based learning, these studies model dMRI features at points in continuous space off the voxel grid in order to propagate streamlines, or virtual estimates of axons. However, implementing such models is non-trivial, and an open-source implementation is not yet widely available. Here, we describe a series of considerations for implementing tractography with RNNs and demonstrate they allow one to approximate a deterministic streamline propagator with comparable performance to existing algorithms. We release this trained model and the associated implementations leveraging popular deep learning libraries. We hope the availability of these resources will lower the barrier of entry into this field, spurring further innovation.