Geometric modeling of aortic dissections through convolution surfaces

Luana Petrelli; Antonio Pepe; Antonella Disanto; Christina Gsaxner; Jianning Li; Yuan Jin; Domenico Buongiorno; Antonio Brunetti; Vitoantonio Bevilacqua; Jan Egger

doi:10.1117/12.2628187

4 April 2022 Geometric modeling of aortic dissections through convolution surfaces

Luana Petrelli, Antonio Pepe, Antonella Disanto, Christina Gsaxner, Jianning Li, Yuan Jin, Domenico Buongiorno, Antonio Brunetti, Vitoantonio Bevilacqua, Jan Egger

Author Affiliations +

Proceedings Volume 12037, Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications; 120370V (2022) https://doi.org/10.1117/12.2628187
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Cardiovascular diseases are one of the strongest burdens in healthcare. If misdiagnosed, they can lead to life-threatening complications. This is especially true for aortic dissections, which may require immediate surgery depending on the categorization and still lead to late adverse events. Aortic dissection occurs when the aortic duct splits into two blood streams, the true and false lumina. The morphological characteristics of the aorta are therefore crucial for a clinician and provide vital support since they can be used to extract significant information for surgery and treatment planning. In this work, we revive a successful modeling technique – convolution surfaces – to model the lumina in aortic dissections. The skeleton of the lumina and local radial information are used to represent the true and the false lumen through convolution of local segments. Additionally, we introduce an optimization strategy based on a genetic algorithm to create the separation caused by the dissection flap.

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Luana Petrelli, Antonio Pepe, Antonella Disanto, Christina Gsaxner, Jianning Li, Yuan Jin, Domenico Buongiorno, Antonio Brunetti, Vitoantonio Bevilacqua, and Jan Egger "Geometric modeling of aortic dissections through convolution surfaces", Proc. SPIE 12037, Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications, 120370V (4 April 2022); https://doi.org/10.1117/12.2628187

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