Parallelized Global Brain Tractography

Abstract

Most brain tractography algorithms suffer from lower accuracy, because they use only information in a certain neighborhood and reconstruct the tracts independently. Global brain tractography algorithms compensate the lack of accuracy of those local algorithms in certain areas by optimizing the whole tractogram. The global tractography approach by Reisert et al. showed the best results in the Fiber Cup contest, but the runtime is still a matter for a medical application. In this paper we present the non-trivial parallelization of this global tractography algorithm. The parallelization exploits properties of the algorithm and modifies the algorithm where necessary. We compare the runtimes of the serial and the parallel variant and show that the outcomes of the parallel variant are of the same quality as those of the serial algorithm. The experiments proof also that the parallelization scales well for real world datasets.