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Magnetic Resonance Imaging

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Updated: May 22, 2025

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Published on: December 18, 2016

An analysis of performance bottlenecks in MRI preprocessing.

Mathieu Dugré¹, Yohan Chatelain¹, Tristan Glatard^1,2

¹Concordia University, Department of Computer Science and Software Engineering, 1455 Blvd. De Maisonneuve Ouest, Montreal, Quebec H3G 1M8, Canada.

|March 12, 2025

Summary

This summary is machine-generated.

Optimizing magnetic resonance imaging (MRI) preprocessing is crucial for neuroimaging. This study identified key performance bottlenecks, like linear interpolation and data access, in common MRI pipelines to guide future improvements.

Keywords:

MRI neuroimaging performance preprocessing profiling

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Last Updated: May 22, 2025

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Published on: April 19, 2016

Area of Science:

Neuroimaging Analysis
Computational Neuroscience
Medical Image Processing

Background:

Magnetic resonance imaging (MRI) preprocessing is essential for accurate neuroimaging analysis.
Computational demands of MRI preprocessing pipelines hinder large-scale studies and clinical use.
Existing acceleration methods (HPC, deep learning) have accessibility and hardware limitations.

Purpose of the Study:

To identify and characterize performance bottlenecks in commonly used MRI preprocessing pipelines.
To provide insights for optimizing MRI preprocessing efficiency and accessibility.

Main Methods:

Utilized Intel VTune profiler to analyze MRI preprocessing pipelines from ANTs, FMRIB, and FreeSurfer.
Characterized CPU time contributions and identified specific computationally intensive functions.
Investigated data access patterns and potential software-related performance issues.

Main Results:

A small number of functions accounted for the majority of CPU time, with linear interpolation being the primary contributor.
Data access emerged as a significant performance bottleneck.
Identified a bug in the Insight Segmentation and Registration Toolkit affecting ANTs performance and a potential OpenMP scaling issue in FreeSurfer's recon-all.

Conclusions:

Understanding specific bottlenecks, such as linear interpolation and data access, is key to optimizing MRI preprocessing.
Software-specific issues (ANTs, FreeSurfer) were identified, offering targets for immediate improvement.
These findings serve as a valuable reference for developing more efficient and accessible neuroimaging analysis tools.