Using GPUs to accelerate computational diffusion MRI: from microstructure estimation to tractography and connectomes

Hernandez-Fernandez, Moises; Reguly, Istvan; Jbabdi, Saad; Giles, Mike; Smith, Stephen; Sotiropoulos, Stamatios N.

Abstract

The great potential of computational diffusion MRI (dMRI) relies on indirect inference of tissue microstructure and brain connections, since modelling and tractography frameworks map diffusion measurements to neuroanatomical features. This mapping however can be computationally highly expensive, particularly given the trend of increasing dataset sizes and the complexity in biophysical modelling. Limitations on computing resources can restrict data exploration and methodology development. A step forward is to take advantage of the computational power offered by recent parallel computing architectures, especially Graphics Processing Units (GPUs). GPUs are massive parallel processors that offer trillions of floating point operations per second, and have made possible the solution of computationally-intensive scientific problems that were intractable before. However, they are not inherently suited for all problems. Here, we present two different frameworks for accelerating dMRI computations using GPUs that cover the most typical dMRI applications: a framework for performing biophysical modelling and microstructure estimation, and a second framework for performing tractography and long-range connectivity estimation. The former provides a front-end and automatically generates a GPU executable file from a user-specified biophysical model, allowing accelerated non-linear model fitting in both deterministic and stochastic ways (Bayesian inference). The latter performs probabilistic tractography, it can generate whole-brain connectomes and supports new functionality for imposing anatomical constraints, such as inherent consideration of surface meshes (GIFTI files) along with volumetric images. We validate the frameworks against well-established CPU-based implementations and we show that despite the very different challenges for parallelising these problems, a single GPU achieves better performances than 200 CPU cores thanks to our parallel designs.

Citation

Hernandez-Fernandez, M., Reguly, I., Jbabdi, S., Giles, M., Smith, S., & Sotiropoulos, S. N. (2019). Using GPUs to accelerate computational diffusion MRI: from microstructure estimation to tractography and connectomes. NeuroImage, 188, 598-615. https://doi.org/10.1016/j.neuroimage.2018.12.015

• Journal Article Type: Article
• Acceptance Date: Dec 7, 2018
• Online Publication Date: Dec 8, 2018
• Publication Date: 2019-03
• Deposit Date: Dec 7, 2018
• Publicly Available Date: Jan 3, 2019
• Journal: NeuroImage
• Print ISSN: 1053-8119
• Electronic ISSN: 1095-9572
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 188
• Pages: 598-615
• DOI: https://doi.org/10.1016/j.neuroimage.2018.12.015
• Keywords: GPGPU; Scientific computing; Biophysical modelling; Non-linear optimisation; Bayesian inference; Fibre orientations; Fibre dispersion; Brain connectivity; Medical imaging
• Public URL: https://nottingham-repository.worktribe.com/output/1381259
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S1053811918321591
• Additional Information: This article is maintained by: Elsevier; Article Title: Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes; Journal Title: NeuroImage; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.neuroimage.2018.12.015; Content Type: article; Copyright: Crown Copyright © 2018 Published by Elsevier Inc.
• Contract Date: Dec 7, 2018