Implicit Modeling of Patient-Specific Aortic Dissections with Elliptic Fourier Descriptors

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dc.contributor.authorMistelbauer, Gabrielen_US
dc.contributor.authorRössl, Christianen_US
dc.contributor.authorBäumler, Kathrinen_US
dc.contributor.authorPreim, Bernharden_US
dc.contributor.authorFleischmann, Dominiken_US
dc.contributor.editorBorgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonen_US
dc.date.accessioned2021-06-12T11:02:27Z
dc.date.available2021-06-12T11:02:27Z
dc.date.issued2021
dc.description.abstractAortic 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.en_US
dc.description.number3
dc.description.sectionheadersBio-Medical Image Analysis
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume40
dc.identifier.doi10.1111/cgf.14318
dc.identifier.issn1467-8659
dc.identifier.pages423-434
dc.identifier.urihttps://doi.org/10.1111/cgf.14318
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14318
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectHuman centered computing
dc.subjectScientific visualization
dc.subjectApplied computing
dc.subjectHealth informatics
dc.subjectComputing methodologies
dc.subjectParametric curve and surface models
dc.titleImplicit Modeling of Patient-Specific Aortic Dissections with Elliptic Fourier Descriptorsen_US
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