Abstract
Background The correct estimation of fibre orientations is a crucial step for reconstructing human brain tracts. A popular and extensively used tool for this estimation is Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques (bedpostx), which is able to estimate several fibre orientations per voxel (i.e. crossing fibres) using Markov Chain Monte Carlo (MCMC). However, for fitting a model in a whole diffusion MRI dataset, MCMC can take up to a day to complete on a standard CPU. Recently, this algorithm has been ported to run on GPUs, which can accelerate the process, completing the analysis in minutes or hours. However, few studies have looked at whether the results from the CPU and GPU algorithms differ. In this study, we compared CPU and GPU bedpostx outputs by running multiple trials of both algorithms on the same whole brain diffusion data and compared each distribution of output using Kolmogorov-Smirnov tests.
Results We show that distributions of fibre fraction parameters and principal diffusion direction angles from bedpostx and bedpostx_gpu display few statistically significant differences in shape and are localized sparsely throughout the whole brain. Average output differences are small in magnitude compared to underlying uncertainty.
Conclusions Despite small amount of differences in samples created between CPU and GPU bedpostx algorithms, results are comparable given the difference in operation order and library usage between CPU and GPU bedpostx.