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

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

^*Corresponding author for this work

Institute of Computer Graphics and Vision (7100)

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

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.

Original language	English
Title of host publication	Medical Imaging 2022
Subtitle of host publication	Imaging Informatics for Healthcare, Research, and Applications
Editors	Thomas M. Deserno, Thomas M. Deserno, Brian J. Park
Publisher	SPIE
ISBN (Electronic)	9781510649491
DOIs	https://doi.org/10.1117/12.2628187
Publication status	Published - 2022
Event	Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications - Virtual, Online Duration: 21 Mar 2022 → 27 Mar 2022

Publication series

Name	Progress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume	12037
ISSN (Print)	1605-7422

Conference

Conference	Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications
City	Virtual, Online
Period	21/03/22 → 27/03/22

Keywords

CTA
genetic algorithm
implicit modeling
vessel reconstruction

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Atomic and Molecular Physics, and Optics
Biomaterials
Radiology Nuclear Medicine and imaging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1117/12.2628187

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Petrelli, L., Pepe, A., Disanto, A., Gsaxner, C., Li, J., Jin, Y., Buongiorno, D., Brunetti, A., Bevilacqua, V., & Egger, J. (2022). Geometric modeling of aortic dissections through convolution surfaces. In T. M. Deserno, T. M. Deserno, & B. J. Park (Eds.), Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications Article 120370V (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 12037). SPIE. https://doi.org/10.1117/12.2628187

Geometric modeling of aortic dissections through convolution surfaces. / Petrelli, Luana; Pepe, Antonio; Disanto, Antonella et al.
Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications. ed. / Thomas M. Deserno; Thomas M. Deserno; Brian J. Park. SPIE, 2022. 120370V (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 12037).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

Petrelli, L, Pepe, A, Disanto, A, Gsaxner, C, Li, J, Jin, Y, Buongiorno, D, Brunetti, A, Bevilacqua, V & Egger, J 2022, Geometric modeling of aortic dissections through convolution surfaces. in TM Deserno, TM Deserno & BJ Park (eds), Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications., 120370V, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 12037, SPIE, Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications, Virtual, Online, 21/03/22. https://doi.org/10.1117/12.2628187

Petrelli L, Pepe A, Disanto A, Gsaxner C, Li J, Jin Y et al. Geometric modeling of aortic dissections through convolution surfaces. In Deserno TM, Deserno TM, Park BJ, editors, Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications. SPIE. 2022. 120370V. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE). doi: 10.1117/12.2628187

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note = "Funding Information: This work was supported by the TU Graz LEAD Project “Mechanics, Modeling and Simulation of Aortic Dissection”. L.P. and A.D. were also supported by two Erasmus scholarships awarded by the Polytechnic University of Bari. Publisher Copyright: {\textcopyright} 2022 SPIE.; Medical Imaging 2022: Imaging Informatics for Healthcare, Research, and Applications ; Conference date: 21-03-2022 Through 27-03-2022",

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AU - Buongiorno, Domenico

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AU - Bevilacqua, Vitoantonio

AU - Egger, Jan

N1 - Funding Information: This work was supported by the TU Graz LEAD Project “Mechanics, Modeling and Simulation of Aortic Dissection”. L.P. and A.D. were also supported by two Erasmus scholarships awarded by the Polytechnic University of Bari. Publisher Copyright: © 2022 SPIE.

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N2 - 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.

AB - 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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Geometric modeling of aortic dissections through convolution surfaces

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

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