In radiology, the deployment of automated clinical decision support tools to new institutions is often hindered by inter-site data variability. In MRI, data heterogeneity often arises from differences in acquisition protocols. To overcome this issue, we propose a post-hoc harmonization technique based on generative adversarial networks (GAN). Seventy-seven patients suffering from dementia were scanned with two distinct T1-weighted MP-RAGE protocols. We show that cross-protocol harmonization of brain images using a conditional GAN improves image similarity and reduces the variability of brain morphometry.
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