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Abstract #1050

Protocol harmonization using a generative adversarial network decreases morphometry variability

Veronica Ravano1,2,3, Jean-François Démonet2, Daniel Damian2, Reto Meuli2, Gian Franco Piredda1,2,3, Till Huelnhagen1,2,3, Bénédicte Maréchal1,2,3, Jean-Philippe Thiran2,3, Tobias Kober1,2,3, and Jonas Richiardi2
1Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 2Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 3LTS5, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland


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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