Continuous Automated Analysis Workflow for MRS Studies

Helge Jörn Zöllner, Christopher W. Davies-Jenkins, Erik G. Lee, Timothy J. Hendrickson, William T. Clarke, Richard A.E. Edden, Jessica L. Wisnowski, Aaron T. Gudmundson, Georg Oeltzschner

Research output: Contribution to journalArticlepeer-review

Abstract

Magnetic resonance spectroscopy (MRS) can non-invasively measure levels of endogenous metabolites in living tissue and is of great interest to neuroscience and clinical research. To this day, MRS data analysis workflows differ substantially between groups, frequently requiring many manual steps to be performed on individual datasets, e.g., data renaming/sorting, manual execution of analysis scripts, and manual assessment of success/failure. Manual analysis practices are a substantial barrier to wider uptake of MRS. They also increase the likelihood of human error and prevent deployment of MRS at large scale. Here, we demonstrate an end-to-end workflow for fully automated data uptake, processing, and quality review. The proposed continuous automated MRS analysis workflow integrates several recent innovations in MRS data and file storage conventions. They are efficiently deployed by a directory monitoring service that automatically triggers the following steps upon arrival of a new raw MRS dataset in a project folder: (1) conversion from proprietary manufacturer file formats into the universal format NIfTI-MRS; (2) consistent file system organization according to the data accumulation logic standard BIDS-MRS; (3) executing a command-line executable of our open-source end-to-end analysis software Osprey; (4) e-mail delivery of a quality control summary report for all analysis steps. The automated architecture successfully completed for a demonstration dataset. The only manual step required was to copy a raw data folder into a monitored directory. Continuous automated analysis of MRS data can reduce the burden of manual data analysis and quality control, particularly for non-expert users and multi-center or large-scale studies and offers considerable economic advantages.

Original languageEnglish (US)
Article number69
JournalJournal of Medical Systems
Volume47
Issue number1
DOIs
StatePublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • BIDS
  • Linear-combination modeling
  • Magnetic resonance spectroscopy
  • NIfTI-MRS
  • Osprey
  • Reproducibility

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