Abstract #2227

Learning cardiac morphology from MR images using a generative adversarial network: a proof of concept study

Davide Piccini^1,2,3, Aurélien Maillot^1,2, John Heerfordt^1,2, Dimitri Van De Ville^4,5, Juerg Schwitter⁶, Matthias Stuber^2,7, Jonas Richiardi^1,2, and Tobias Kober^1,2,3

¹Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland, ²Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, ³LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ⁴Institute of Bioengineering/Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ⁵Department of Radiology and Medical Informatics, University Hospital of Geneva (HUG), Geneva, Switzerland, ⁶Division of Cardiology and Cardiac MR Center, University Hospital of Lausanne (CHUV), Lausanne, Switzerland, ⁷Center for Biomedical Imaging (CIBM), Lausanne, Switzerland

Learning anatomical characteristics from large databases of radiological data could be leveraged to create realistic representations of a specific subject’s anatomy and to provide a personalized clinical assessment by comparison to the acquired data. Here, we extracted 2D patches containing the descending aorta from 297 3D whole-heart MRI acquisitions and trained a Wasserstein generative adversarial network with a gradient penalty term (WGAN-GP). We used the same network to generate realistic versions of the aortic region on masked real images using a loss function that combines a contextual and a perceptual term. Results were qualitatively assessed by an expert reader.

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