An open source toolkit for medical imaging de-identification
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Medical imaging acquired for clinical purposes can have several legitimate secondary uses in research projects and teaching libraries. No commonly accepted solution for anonymising these images exists because the amount of personal data that should be preserved varies case by case. Our objective is to provide a flexible mechanism for anonymising Digital Imaging and Communications in Medicine (DICOM) data that meets the requirements for deployment in multicentre trials.
We reviewed our current de-identification practices and defined the relevant use cases to extract the requirements for the de-identification process. We then used these requirements in the design and implementation of the toolkit. Finally, we tested the toolkit taking as a reference those requirements, including a multicentre deployment.
The toolkit successfully anonymised DICOM data from various sources. Furthermore, it was shown that it could forward anonymous data to remote destinations, remove burned-in annotations, and add tracking information to the header. The toolkit also implements the DICOM standard confidentiality mechanism.
A DICOM de-identification toolkit that facilitates the enforcement of privacy policies was developed. It is highly extensible, provides the necessary flexibility to account for different de-identification requirements and has a low adoption barrier for new users.
KeywordsDigital Imaging and Communications in Medicine (DICOM) Privacy policies Data Protection Act (DPA) De-identification Anonymisation Toolkit Pseudonymisation
This work was funded by the SINAPSE collaboration (www.sinapse.ac.uk), a Pooling Initiative funded by the Scottish Funding Council and the Chief Scientist Office of the Scottish Executive. It was undertaken at the SFC Brain Imaging Research Centre, Division of Clinical Neurosciences, University of Edinburgh (www.sbirc.ed.ac.uk) and the National e-Science Centre, School of Informatics, University of Edinburgh (research.nesc.ac.uk), both members of SINAPSE. Two of the authors, D. Rodríguez González and J. Wardlaw, are funded by the SINAPSE collaboration, while J.I. van Hemert and T. Carpenter are also members of the collaboration. T. Carpenter is funded by the Cohen Charitable Trust and the Wellcome Trust IT grant 077393/Z/05/Z.
Data used for this study were downloaded from the Biomedical Informatics Research Network (BIRN) Data Repository (http://www.nbirn.net/bdr), supported by grants to the BIRN Coordinating Center (U24-RR019701), Function BIRN (U24-RR021992), Morphometry BIRN (U24-RR021382), and Mouse BIRN (U24-RR021760) Testbeds funded by the National Center for Research Resources at the National Institutes of Health, USA.
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