An Analysis of Performance Bottlenecks in MRI Pre-Processing

TL;DR

This paper identifies key performance bottlenecks in MRI pre-processing pipelines, highlighting the dominant functions and data access issues, and provides insights for future optimization efforts.

Contribution

It offers a detailed profiling of MRI pre-processing tools, revealing specific bottlenecks and bugs to guide performance improvements.

Findings

Linear interpolation is the main CPU time contributor.

Data access significantly impacts pipeline performance.

A bug in ITK affects ANTs performance in single-precision.

Abstract

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.