Abstract #1050

Protocol harmonization using a generative adversarial network decreases morphometry variability

Veronica Ravano^1,2,3, Jean-François Démonet², Daniel Damian², Reto Meuli², Gian Franco Piredda^1,2,3, Till Huelnhagen^1,2,3, Bénédicte Maréchal^1,2,3, Jean-Philippe Thiran^2,3, Tobias Kober^1,2,3, 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

Synopsis

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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