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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