Investigating Computational Bottlenecks in MRI Pre-Processing Pipelines

MRI preprocessing is essential for neuroimaging analysis, yet its computational demands present significant challenges, particularly in large-scale studies and certain clinical applications. Although High-Performance Computing (HPC) and Deep Learning methods have been employed to mitigate these challenges, they are often inaccessible due to high costs and hardware requirements. This study aims to identify and understand the primary performance bottlenecks in MRI preprocessing pipelines to enhance their efficiency. Using the Intel VTune profiler, we analyzed commonly used pipelines from the ANTs, FSL, and FreeSurfer toolboxes. Our findings reveal that a small number of functions dominate CPU usage, with linear interpolation being the most significant contributor. We also observed considerable performance impacts due to data access inefficiencies. Additionally, a bug in the ITK library affecting the ANTs pipeline's single-precision mode and potential issues with OpenMP scaling in FreeSurfer's recon-all were identified. These insights offer a foundation for optimizing MRI preprocessing pipelines in future research.