fMRI Anonymizer
This project contains a series of scripts that perform 2 essential steps:
- De-Identification
- De-Facing
The main purpose of this project is to have an "all-purpose" application that can automatize the whole anonymization process on MRI, fMRI data. This includes DICOM, and NIFTI formats.
This application follows a "best-effort" approach in order to comply with HIPAA regulations.
Pre-requisites
Since this is a module that leverages on other packages, there are 2 main dependencies that must be installed within your system prior to install this solution.
You will need:
-
FSL: Please follow this link to install it.
1.1. Make sure to install XQuartz before installing FSL. This is mentioned within their site.
-
dcm2niix: This is a dependency used to convert DICOM into NIFTI format.
2.1. In order to install it, you can safely use brew.
2.2. You can also follow this tutorial.
How to install it?
In order to use this package, you will need to use the following command (recommended using it in a separate environment - like conda or venv):
pip install fmri-anonymizer
How to use it?
This is pretty simple, you have 2 ways to use it:
python -m fmri_anonymizer -i <INPUT_FOLDER> --dicom --anonymize --deface YES -o <OUTPUT_FOLDER>
Or:
fmri_anonymizer -i <INPUT_FOLDER> --dicom --anonymize --deface YES -o <OUTPUT_FOLDER>
Here, a complete example:
python -m fmri_anonymizer -i "<path_source_dicom_files>" --dicom True --nifti True --deface True --anonymize True -o "<output_path>"
How to get some help?
Simply type:
python -m fmri_anonymizer -h
H4ppy H4ck1n6!
1 maintainer
Metadata
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Release files for fmri-anonymizer 0.2.11
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