Related Experiment Video
Updated: Apr 12, 2026

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
Published on: April 5, 2024
De-identification of Medical Images with Retention of Scientific Research Value
Stephen M Moore1, David R Maffitt, Kirk E Smith
1From the Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, St Louis, MO 63110 (S.M.M., D.R.M., K.E.S., K.W.C., B.A.V., L.R.T., F.W.P.); and Leidos Biomedical Research, Bethesda, Md (J.S.K., J.B.F.).
Abstract:
Online public repositories for sharing research data allow investigators to validate existing research or perform secondary research without the expense of collecting new data. Patient data made publicly available through such repositories may constitute a breach of personally identifiable information if not properly de-identified. Imaging data are especially at risk because some intricacies of the Digital Imaging and Communications in Medicine (DICOM) format are not widely understood by researchers. If imaging data still containing protected health information (PHI) were released through a public repository, a number of different parties could be held liable, including the original researcher who collected and submitted the data, the original researcher's institution, and the organization managing the repository. To minimize these risks through proper de-identification of image data, one must understand what PHI exists and where that PHI resides, and one must have the tools to remove PHI without compromising the scientific integrity of the data. DICOM public elements are defined by the DICOM Standard. Modality vendors use private elements to encode acquisition parameters that are not yet defined by the DICOM Standard, or the vendor may not have updated an existing software product after DICOM defined new public elements. Because private elements are not standardized, a common de-identification practice is to delete all private elements, removing scientifically useful data as well as PHI. Researchers and publishers of imaging data can use the tools and process described in this article to de-identify DICOM images according to current best practices.
More Related Videos
04:48Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
12:50Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
Published on: April 14, 2014