Key result
An implicit modeling approach using elliptic Fourier descriptors enables accurate, patient-specific cross-sectional modeling of aortic dissections for high-quality surface representations.
Why the study?
Accurate patient-specific surface representations of aortic dissections are critical for risk prediction and hemodynamic simulations, but currently require time-consuming manual segmentation of CT data.
The use of elliptic Fourier descriptors improves the automated generation of patient-specific 3D surface models for aortic dissections from CT data.
Supports automated CT-based aortic dissection modeling; leaves open clinical validation and workflow integration.
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.
No takes yet. Share an insight, caveat, or question.
Mistelbauer et al. (2021) studied Aortic dissection. Implicit modeling with elliptic Fourier descriptors vs. Traditional spline-based cross-sectional model was evaluated on Generation of surface and wall representations of flow channels, communications, and branches. An implicit modeling approach using elliptic Fourier descriptors enables accurate, patient-specific cross-sectional modeling of aortic dissections for high-quality surface representations.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: