Effects of defacing whole head MRI on neuroanalysis

Chenyu Gao; Linghao Jin; Jerry L. Prince; Aaron Carass

doi:10.1117/12.2613175

4 April 2022 Effects of defacing whole head MRI on neuroanalysis

Chenyu Gao, Linghao Jin, Jerry L. Prince, Aaron Carass

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120323W (2022) https://doi.org/10.1117/12.2613175
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Recent advances in magnetic resonance (MR) scanner quality and the rapidly improving nature of facial recognition software have necessitated the introduction of MR defacing algorithms to protect patient privacy. As a result, there are a number of MR defacing algorithms available to the neuroimaging community, with several appearing in just the last five years. These various approaches have qualities that have been explored with respect to skull stripping masks or identifiability of the patient in previous works. However, to our knowledge there has been no evaluation of the subsequent impact of these defacing algorithms on a neuroimaging pipeline. In this work, we use six MR defacing algorithms on 179 subjects from the OASIS-3 cohort and 21 subjects from the Kirby 21 dataset, then apply a neuroimaging pipeline to the resultant defaced images. We compare the consistency of the output from the pipeline using the defaced images with the output of the same pipeline without defacing the MR data.

Conference Presentation

Citation Download Citation

Chenyu Gao, Linghao Jin, Jerry L. Prince, and Aaron Carass "Effects of defacing whole head MRI on neuroanalysis", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120323W (4 April 2022); https://doi.org/10.1117/12.2613175

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