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Title: Implicit modeling of patient-specific aortic dissections with elliptic fourier descriptors
Author(s): Mistelbauer, Gabriel
Rössl, Christian
Bäumler, K.
Preim, BernhardLook up in the Integrated Authority File of the German National Library
Fleischmann, D.
Issue Date: 2021
Type: Article
Language: English
URN: urn:nbn:de:gbv:ma9:1-1981185920-946616
Subjects: Human-centered computing
Scientific visualization
Health informatics
Parametric curve and surface models
Abstract: Aortic dissection is a life-threatening vascular disease characterized by abrupt formation of a new flow channel (false lumen) within the aortic wall. Survivors of the acute phase remain at high risk for late complications, such as aneurysm formation, rupture, and death. Morphologic features of aortic dissection determine not only treatment strategies in the acute phase (surgical vs. endovascular vs. medical), but also modulate the hemodynamics in the false lumen, ultimately responsible for late complications. Accurate description of the true and false lumen, any communications across the dissection membrane separating the two lumina, and blood supply from each lumen to aortic branch vessels is critical for risk prediction. Patient-specific surface representations are also a prerequisite for hemodynamic simulations, but currently require time-consuming manual segmentation of CT data. We present an aortic dissection cross-sectional model that captures the varying aortic anatomy, allowing for reliable measurements and creation of high-quality surface representations. In contrast to the traditional spline-based cross-sectional model, we employ elliptic Fourier descriptors, which allows users to control the accuracy of the cross-sectional contour of a flow channel. We demonstrate (i) how our approach can solve the requirements for generating surface and wall representations of the flow channels, (ii) how any number of communications between flow channels can be specified in a consistent manner, and (iii) how well branches connected to the respective flow channels are handled. Finally, we discuss how our approach is a step forward to an automated generation of surface models for aortic dissections from raw 3D imaging segmentation masks.
Open Access: Open access publication
License: (CC BY-NC-ND 4.0) Creative Commons Attribution NonCommercial NoDerivatives 4.0(CC BY-NC-ND 4.0) Creative Commons Attribution NonCommercial NoDerivatives 4.0
Sponsor/Funder: Projekt DEAL 2021
Journal Title: Computer graphics forum
Publisher: Wiley-Blackwell
Publisher Place: Oxford
Volume: 40
Issue: 3
Original Publication: 10.1111/cgf.14318
Page Start: 423
Page End: 434
Appears in Collections:Fakultät für Informatik (OA)

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