{"title":"pyfMRIqc: A Software Package for Raw fMRI Data Quality Assurance","authors":"B. Williams, Michael Q. Lindner","doi":"10.5334/jors.280","DOIUrl":null,"url":null,"abstract":"pyfMRIqc is a tool for checking the quality of raw functional magnetic resonance imaging (fMRI) data. pyfMRIqc produces a range of output files which can be used to identify fMRI data quality issues such as artefacts, motion, signal loss etc. This tool creates a number of 3D and 4D NIFTI files that can be used for in depth quality assurance. Additionally, 2D images are created for each NIFTI file for a quick overview. These images and other information (e.g. about signal-to-noise ratio, scan parameters, etc.) are combined in a user-friendly HTML output file. pyfMRIqc is written entirely in Python and is available under a GNU GPL3 license on GitHub (https://drmichaellindner.github.io/pyfMRIqc/). pyfMRIqc can be used from the command line and therefore can be included as part of a processing pipeline or used to quality-check a series of datasets using batch scripting. The quality assurance of a single dataset can also be performed via dialog boxes.","PeriodicalId":37323,"journal":{"name":"Journal of Open Research Software","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Open Research Software","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5334/jors.280","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 5
Abstract
pyfMRIqc is a tool for checking the quality of raw functional magnetic resonance imaging (fMRI) data. pyfMRIqc produces a range of output files which can be used to identify fMRI data quality issues such as artefacts, motion, signal loss etc. This tool creates a number of 3D and 4D NIFTI files that can be used for in depth quality assurance. Additionally, 2D images are created for each NIFTI file for a quick overview. These images and other information (e.g. about signal-to-noise ratio, scan parameters, etc.) are combined in a user-friendly HTML output file. pyfMRIqc is written entirely in Python and is available under a GNU GPL3 license on GitHub (https://drmichaellindner.github.io/pyfMRIqc/). pyfMRIqc can be used from the command line and therefore can be included as part of a processing pipeline or used to quality-check a series of datasets using batch scripting. The quality assurance of a single dataset can also be performed via dialog boxes.