Anonymization and validation of 3-dimensional volumetric renderings of Computed Tomography (CT) data using commercially available T1W MRI-based algorithms

Rahil Patel; Destie Provenzano; Murray Loew

doi:10.1117/12.2654411

10 April 2023 Anonymization and validation of 3-dimensional volumetric renderings of Computed Tomography (CT) data using commercially available T1W MRI-based algorithms

Rahil Patel, Destie Provenzano, Murray Loew

Author Affiliations +

Proceedings Volume 12469, Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications; 124690A (2023) https://doi.org/10.1117/12.2654411
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Previous studies have demonstrated that 3-dimensional (3D) volumetric renderings of magnetic resonance imaging (MRI) brain scan imaging data can be used to identify patients using facial recognition algorithms. We have shown that facial features can be identified on SIM-CT (simulation computed tomography) images for radiation oncology and mapped to face images from a database. We now seek to determine whether CT images can be anonymized using anonymization software that was designed for T1W MRI data. Our study examines (1) the ability of off-the-shelf anonymization algorithms to anonymize CT data, and (2) the ability of facial recognition algorithms to then identify whether faces could be detected from a database of facial images. This study generated 3D renderings from open-source CT scans of two patients from The Cancer Imaging Archive (TCIA) database. Data were then anonymized using AFNI (deface, reface, 3Dskullstrip), and FSL (deface and BET). Anonymized data were compared to the original renderings and also passed through facial recognition algorithms (Face_compare, VGG-Face, Facenet, DLib, and SFace) using a publicly available face database (Labeled Faces in the Wild) to determine what matches could be found. Our study found that all modules were able to process CT data in addition to T1W and T2W data and that data were successfully anonymized by AFNI's 3Dskullstrip and FSL's BET: they did not match the control image across all facial recognition algorithms. Our study demonstrates the importance of continued vigilance for patient privacy in publicly shared datasets and the importance of evaluation of anonymization methods for CT data.

Conference Presentation

Citation Download Citation

Rahil Patel, Destie Provenzano, and Murray Loew "Anonymization and validation of 3-dimensional volumetric renderings of Computed Tomography (CT) data using commercially available T1W MRI-based algorithms", Proc. SPIE 12469, Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications, 124690A (10 April 2023); https://doi.org/10.1117/12.2654411

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