OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
Authors: Clete A Kushida; Deborah A Nichols; Rik Jadrnicek; Ric Miller; James K Walsh; Kara Griffin Journal: Med Care Date: 2012-07 Impact factor: 2.983
Authors: Wayne Newhauser; Timothy Jones; Stuart Swerdloff; Warren Newhauser; Mark Cilia; Robert Carver; Andy Halloran; Rui Zhang Journal: Comput Biol Med Date: 2014-07-26 Impact factor: 4.589
Authors: Arsalan Shahid; Mehran H Bazargani; Paul Banahan; Brian Mac Namee; Tahar Kechadi; Ceara Treacy; Gilbert Regan; Peter MacMahon Journal: Healthcare (Basel) Date: 2022-04-19
Authors: Susan D Shenkin; Cyril Pernet; Thomas E Nichols; Jean-Baptiste Poline; Paul M Matthews; Aad van der Lugt; Clare Mackay; Linda Lanyon; Bernard Mazoyer; James P Boardman; Paul M Thompson; Nick Fox; Daniel S Marcus; Aziz Sheikh; Simon R Cox; Devasuda Anblagan; Dominic E Job; David Alexander Dickie; David Rodriguez; Joanna M Wardlaw Journal: Neuroimage Date: 2017-02-14 Impact factor: 6.556