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Continuous Automated Analysis Workflow for MRS Studies.
Zöllner, Helge Jörn; Davies-Jenkins, Christopher W; Lee, Erik G; Hendrickson, Timothy J; Clarke, William T; Edden, Richard A E; Wisnowski, Jessica L; Gudmundson, Aaron T; Oeltzschner, Georg.
Afiliação
  • Zöllner HJ; Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA. hzoelln2@jhmi.edu.
  • Davies-Jenkins CW; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA. hzoelln2@jhmi.edu.
  • Lee EG; Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA.
  • Hendrickson TJ; F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA.
  • Clarke WT; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
  • Edden RAE; Informatics Institute, University of Minnesota, Minneapolis, MN, USA.
  • Wisnowski JL; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
  • Gudmundson AT; Informatics Institute, University of Minnesota, Minneapolis, MN, USA.
  • Oeltzschner G; Wellcome Centre for Integrative Neuroimaging, Department of Clinical Neurosciences, FMRIB, University of Oxford, Oxford, Nuffield, UK.
J Med Syst ; 47(1): 69, 2023 Jul 07.
Article em En | MEDLINE | ID: mdl-37418036
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software Tipo de estudo: Clinical_trials / Guideline Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software Tipo de estudo: Clinical_trials / Guideline Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article