MRI anonymizations, including face removal, are necessary for clinical data archiving and sharing. Segmentation based methods have been developed for semi-automated face removal on brain MRI. Meanwhile, the conventional methods are inefficient and unreliable, as the images have to be pre-processed and fed in the software manually. Deep learning-based methods are highly efficient in image-to-image translation on large scale databases. In this study, we utilized a cycle generative adversarial network to anonymize brain MRI data. The model showed reliable performance when testing on T1-weighted images, and we also extend it to the unseen MPRAGE images, targeting different brain MRI contrasts.
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