Accelerated GVE File Parsing for Philips MRI Sequences Using Julia

Motivation: MRI simulation and reconstruction projects often require loading entire MRI sequences. Existing solutions in MATLAB tend to be slow and inefficient. Goal(s): This work aims to provide a fast Julia implementation for loading Philips GVE files containing all relevant MRI sequence parameters. Approach: We exploit Julia's superior file handling and multithreading capabilities to develop a module that can easily be integrated into other MRI projects. Performance and accuracy are evaluated by comparing to an existing MATLAB solution. Results: Our Julia module achieves acceleration factors up to 29.16 while maintaining a high accuracy of the loaded sequences. Impact: Long load times of MRI sequences can significantly slow down simulation and reconstruction projects. The proposed Julia module provides a fast and accurate solution that can easily be integrated into other projects.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/1959
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Accelerated GVE File Parsing for Philips MRI Sequences Using Julia

Christina Gräf, Alexander Rauscher, Alexander Jaffray, Julian Kloiber
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Generative Adversarial Networks and Image Synthesis
article

Accelerated GVE File Parsing for Philips MRI Sequences Using Julia

Christina Gräf, Alexander Rauscher, Alexander Jaffray, Julian Kloiber
article en

Abstract

Motivation: MRI simulation and reconstruction projects often require loading entire MRI sequences. Existing solutions in MATLAB tend to be slow and inefficient. Goal(s): This work aims to provide a fast Julia implementation for loading Philips GVE files containing all relevant MRI sequence parameters. Approach: We exploit Julia's superior file handling and multithreading capabilities to develop a module that can easily be integrated into other MRI projects. Performance and accuracy are evaluated by comparing to an existing MATLAB solution. Results: Our Julia module achieves acceleration factors up to 29.16 while maintaining a high accuracy of the loaded sequences. Impact: Long load times of MRI sequences can significantly slow down simulation and reconstruction projects. The proposed Julia module provides a fast and accurate solution that can easily be integrated into other projects.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 76%
Generative Adversarial Networks and Image Synthesis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Accelerated GVE File Parsing for Philips MRI Sequences Using Julia — Christina Gräf, Alexander Rauscher, et al. · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2025) | TGRS Research Map | TGRS