Package: bidsonym
DescriptionABIDSAppfor the de-identification of neuroimaging data.BIDSonymgathers all T1w images from a BIDS dataset and applies one of several popular de-identification algorithms. It currently supports:MRI deface,Pydeface,Quickshearandmridefacer.Additionally, the user can choose to evaluate the sidecar JSON files regarding potentially sensitive information,
like for example participant names and define a list of fields which information should be deleted.Using BIDSonym can help you can make collected neuroimaging data available for others without violating subjects’ privacy or anonymity (depending on the regulations of the country you’re in).UsageThis App has the following command line arguments:usage:  run.py [-h][–participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL …]][–deid {pydeface,mri_deface,quickshear}][–del_nodeface {del,no_del}][–deface_t2w][–check_meta][–del_meta META_DATA_FIELD [META_DATA_FIELD …]][–brainextraction {bet,nobrainer}][–bet_frac BET_FRAC]bids_dir {participant,group}a BIDS app for de-identification of neuroimaging datapositional arguments:bids_dir              The directory with the input dataset formattedaccording to the BIDS standard.output_dir            The directory where the not de-identified raw files should be stored,in case you decide to keep them.{participant,group}   Level of the analysis that will be performed. Multipleparticipant level analyses can be run independently
(in parallel) using the same output_dir.optional arguments:–participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL …]The label(s) of the participant(s) that should be
analyzed. The label corresponds to
sub-<participant_label> from the BIDS spec (so it does
not include “sub-“). If this parameter is not provided
all subjects should be analyzed. Multiple participants
can be specified with a space separated list.–deid {pydeface,mri_deface,quickshear}Approach to use for de-identifictation.–deface_t2wDeface T2w images by using defaced T1w image as deface-mask.–check_meta META_DATA_FIELD [META_DATA_FIELD …]Indicate which information from the image and.jsonmeta-data files should be check for potentially problematic information.
Indicate strings that should be searched for.
The results will be saved tosourcedata/.–del_meta META_DATA_FIELD [META_DATA_FIELD …]Indicate (via strings) if and which information from the.jsonmeta-data
files should be deleted. If so, the original.jsonfiles
will be copied tosourcedata/.–brainextraction {BET, no_brainer}What algorithm should be used for pre-defacing brain extraction
(outputs will be used in quality control).–bet_frac [BET_FRAC]In case BET is used for pre-defacing brain extraction, provide a Frac value.–skip_bids_validationAssume the input dataset is BIDS compliant and skip the validation (default: False).-vBIDS-App version.Run it in participant level mode (for one participant):docker run -i --rm \
            -v /Users/peer/ds005:/bids_dataset \
            peerherholz/bidsonym \
            /bids_dataset \
            participant --deid pydeface --del_meta 'InstitutionAddress' \
            --participant_label 01
            --brainextraction bet --bet_frac 0.5Run it in group level mode (for all participants):docker run -i --rm \
           -v /Users/peer/ds005:/bids_dataset \
           peerherholz/bidsonym \
           /bids_dataset  group --deid pydeface --del_meta 'InstitutionAddress' \
           --brainextraction bet --bet_frac 0.5InstallationFollowing theBIDS apps standardit is recommend to install and use BIDSonym in its Docker or Singularity form. To get the BIDSonym Docker image, you need toinstall dockerand within the terminal of your choice type:docker pull peerherholz/bidsonymTo get its Singularity version, you need toinstall singularityand within the terminal of your choice type:singularity pull PeerHerholz/BIDSonymDocumentationBIDSonym’s documentation can be foundhere.How to report errorsRunning into any bugs :beetle:? Check out theopen issuesto see if we’re already working on it. If not, open up a new issue and we will check it out when we can!How to contributeThank you for considering contributing to our project! Before getting involved, please review ourCode of Conduct. Next, you can reviewopen issuesthat we are looking for help with. If you submit a new pull request please be as detailed as possible in your comments. Please also have a look at ourcontribution guidelines.AcknowledgementsPlease acknowledge this work by mentioning explicitly the name of this software
(BIDSonym) and the version, along with a link to theGitHub repositoryor the Zenodo reference.
For more details, please seecitation.SupportThis work is supported in part by funding provided byBrain Canada, in partnership withHealth Canada, for theCanadian Open Neuroscience Platform initiative.Furthermore, the project is supported by [Repronim](https://www.repronim.org/) under NIH-NIBIB P41 EB019936.