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Revolutionizing MRI Data Processing Using FSL: Preliminary Findings with the Fugaku Supercomputer

Tianxiang Lyu, Wataru Uchida, Zhe Sun, Christina Andica, Keita Tokuda, Rui Zou, Jie Mao, Keigo Shimoji, Koji Kamagata, Mitsuhisa Sato, Ryutaro Himeno, Shigeki Aoki

TL;DR

The tensor-based measurements and subcortical structure segmentations performed on Fugaku supercomputer were highly consistent with those from conventional systems, demonstrating its reliability and significantly reduced processing time.

Abstract

The amount of Magnetic resonance imaging data has grown tremendously recently, creating an urgent need to accelerate data processing, which requires substantial computational resources and time. In this preliminary study, we applied FMRIB Software Library commands on T1-weighted and diffusion-weighted images of a single young adult using the Fugaku supercomputer. The tensor-based measurements and subcortical structure segmentations performed on Fugaku supercomputer were highly consistent with those from conventional systems, demonstrating its reliability and significantly reduced processing time.

Revolutionizing MRI Data Processing Using FSL: Preliminary Findings with the Fugaku Supercomputer

TL;DR

The tensor-based measurements and subcortical structure segmentations performed on Fugaku supercomputer were highly consistent with those from conventional systems, demonstrating its reliability and significantly reduced processing time.

Abstract

The amount of Magnetic resonance imaging data has grown tremendously recently, creating an urgent need to accelerate data processing, which requires substantial computational resources and time. In this preliminary study, we applied FMRIB Software Library commands on T1-weighted and diffusion-weighted images of a single young adult using the Fugaku supercomputer. The tensor-based measurements and subcortical structure segmentations performed on Fugaku supercomputer were highly consistent with those from conventional systems, demonstrating its reliability and significantly reduced processing time.
Paper Structure (18 sections, 2 equations, 6 figures)

This paper contains 18 sections, 2 equations, 6 figures.

Table of Contents

  1. Abstract
  2. Introduction
  3. Method
  4. Study Participant
  5. Data Acquisition
  6. Processing Pipelines
  7. System Configuration
  8. Fugaku Supercomputer
  9. Conventional node
  10. Data Parallelism Strategies
  11. NUMA-Aware Optimizations
  12. Bulk Jobs
  13. Verification of pipeline
  14. Results
  15. Diffusion Tensor Imaging
  16. ...and 3 more sections

Figures (6)

  • Figure 1: Processing pipeline set on supercomputers
  • Figure 2: Data Parallelism Processing Framework
  • Figure 3: Comparison between Fugaku and Conventional Node
  • Figure 4: Scatter Plot of DTIFIT Values
  • Figure 5: Structure and Dice Coefficient
  • ...and 1 more figures