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.

Medical image processing on the GPU – Past, present and future

“…A framerate of about 20 Hz was achieved for 1,000 concurrent seeds. Recently, Hernandez et al (2012) accelerated a Bayesian approach to estimation of fiber orientations and uncertainties, used in the FMRIB software library (FSL). In their implementation, each GPU thread performs a Levenberg-Marquardt optimization and a posterior estimation through MCMC.…”

Harnessing graphics processing units for improved neuroimaging statistics

“…For DTI, GPUs have been used to accelerate a Bayesian approach to stochastic brain connectivity mapping (McGraw & Nadar, 2007) and a Bayesian framework for estimation of fiber orientations and their uncertainties (Hernandez et al, 2012). This framework normally requires more than 24 hours of processing time for a single subject, compared to 17 minutes with a GPU.…”

GPU-accelerated dynamic functional connectivity analysis for functional MRI data using OpenCL

“…Since their introduction, OpenCL-based applications attracted researchers to use this tool in various scientific and engineering applications [5][6][7]. OpenCL-based parallel programming techniques are also utilized for speeding up computing methods in bioinformatics [8][9][10][11][12][13]. An example of parallelizable computing methods used in medical imaging is dynamic functional connectivity (DFC) analysis, which basically performs sliding-window time-series analysis of temporal neuroimaging data from the brain [14][15][16][17].…”