Acceleration of MRI analysis using multicore and manycore paradigms

Abstract

Magnetic resonance imaging (MRI) of the brain is a safe and painless test that uses a magnetic field and radio waves to produce detailed images of the brain. FreeSurfer is a tool neuroscientists use to create models of structures in the brain. An average MRI analysis using FreeSurfer takes around 7 h on a central processing unit with 4 cores. Since execution time is so high, researchers are working on different ways to parallelize the software. Most efforts are concentrated on parallelization using multicore, specifically with OpenMP (an implementation of multithreading) reducing execution time around 20%. In this paper, we further accelerate the analysis time for FreeSurfer using the manycore processors, special multicore processors containing from dozens to thousands simpler independent cores. Specifically, we will use graphics processing unit (GPU) a manycore with thousands of simpler cores. Multicore and manycore using GPU acceleration are not mutually exclusive (we will call it GPU acceleration from now on), and we present an implementation that uses both types of accelerations (multicore and GPU). Results show that execution times using both accelerations reduce the analysis time by 70%. Manycore processors are specialist multicore processors designed for a high degree of parallel processing, containing numerous simpler, independent processor cores (from a few tens of cores to thousands or more). Manycore processors are used extensively in embedded computers and high-performance computing.