Accelerating Fibre Orientation Estimation from Diffusion Weighted Magnetic Resonance Imaging Using GPUs

Abstract

With the performance of central processing units (CPUs) having effectively reached a limit, parallel processing offers an alternative for applications with high computational demands. Modern graphics processing units (GPUs) are massively parallel processors that can execute simultaneously thousands of light-weight processes. In this study, we propose and implement a parallel GPU-based design of a popular method that is used for the analysis of brain magnetic resonance imaging (MRI). More specifically, we are concerned with a model-based approach for extracting tissue structural information from diffusion-weighted (DW) MRI data. DW-MRI offers, through tractography approaches, the only way to study brain structural connectivity, non-invasively and in-vivo. We parallelise the Bayesian inference framework for the ball & stick model, as it is implemented in the tractography toolbox of the popular FSL software package (University of Oxford). For our implementation, we utilise the Compute Unified Device Architecture (CUDA) programming model. We show that the parameter estimation, performed through Markov Chain Monte Carlo (MCMC), is accelerated by at least two orders of magnitude, when comparing a single GPU with the respective sequential single-core CPU version. We also illustrate similar speed-up factors (up to 120x) when comparing a multi-GPU with a multi-CPU implementation.

Advances in diffusion MRI acquisition and processing in the Human Connectome Project

“…We employed the Compute Unified Device Architecture (CUDA) programming model to develop GPU-based toolboxes for the fibre orientation estimation and distortion correction stages. The principles of these designs and implementations have been presented in (Hernandez et al, 2012; Hernandez et al, 2013), and speed-up factors of at least two orders of magnitude have been achieved for the Bayesian estimation of fibre orientations. More specifically, for a typical HCP dMRI dataset, the MCMC-based estimation of the multi-shell deconvolution model (Jbabdi et al, 2012), that returns the posterior distribution of the parameter given the data, takes 15–20 minutes on a GPU cluster, while requiring 25–30 hours on a CPU cluster with the same number of computing nodes.…”

Accelerated Microstructure Imaging via Convex Optimization (AMICO) from diffusion MRI data

“…On the one hand, all these techniques have demonstrated the practical possibility to estimate microstructural information from dMRI data in addition to just the orientation of the fiber populations in a voxel, and the estimated microstructural indices have been shown to agree very well with known anatomical patterns observed with histology Zhang et al, 2012;Dyrby et al, 2013). On the other hand, however, the non-linear routines usually employed to fit these models, as well as other diffusion modalities (Hernández et al, 2013;Chang et al, 2014), are computationally very intensive and cause practical problems for their application in clinical studies, especially with large cohorts of subjects.…”

VERDICT‐AMICO: Ultrafast fitting algorithm for non‐invasive prostate microstructure characterization

“…Recently, ultrafast‐fitting algorithms have been developed to address the high computational cost of model‐based microstructure‐imaging techniques . Graphical processing units (GPUs) provide a brute‐force solution, using a parallelized approach, to reduce the computational time as in References and .…”

“…Recently, ultrafast‐fitting algorithms have been developed to address the high computational cost of model‐based microstructure‐imaging techniques . Graphical processing units (GPUs) provide a brute‐force solution, using a parallelized approach, to reduce the computational time as in References and . Although some overhead lies in GPU‐based design and implementation, once adapted for GPU platforms the fitting time can be reduced by several orders of magnitude compared with standard central processing units, and is limited only by the number of cores on the GPU.…”