  Magnetic Resonance Image (MRI) pre-processing is a critical step for
neuroimaging analysis. However, the computational cost of MRI pre-processing
pipelines is a major bottleneck for large cohort studies and some clinical
applications. While High-Performance Computing (HPC) and, more recently, Deep
Learning have been adopted to accelerate the computations, these techniques
require costly hardware and are not accessible to all researchers. Therefore,
it is important to understand the performance bottlenecks of MRI pre-processing
pipelines to improve their performance. Using Intel VTune profiler, we
characterized the bottlenecks of several commonly used MRI-preprocessing
pipelines from the ANTs, FSL, and FreeSurfer toolboxes. We found that few
functions contributed to most of the CPU time, and that linear interpolation
was the largest contributor. Data access was also a substantial bottleneck. We
identified a bug in the ITK library that impacts the performance of ANTs
pipeline in single-precision and a potential issue with the OpenMP scaling in
FreeSurfer recon-all. Our results provide a reference for future efforts to
optimize MRI pre-processing pipelines.
