A Framework for the Automatic Generation of Surface Topologies for Abdominal Aortic Aneurysm Models
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Patient-specific abdominal aortic aneurysms (AAAs) are characterized by local curvature changes, which we assess using a feature-based approach on topologies representative of the AAA outer wall surface. The application of image segmentation methods yields 3D reconstructed surface polygons that contain low-quality elements, unrealistic sharp corners, and surface irregularities. To optimize the quality of the surface topology, an iterative algorithm was developed to perform interpolation of the AAA geometry, topology refinement, and smoothing. Triangular surface topologies are generated based on a Delaunay triangulation algorithm, which is adapted for AAA segmented masks. The boundary of the AAA wall is represented using a signed distance function prior to triangulation. The irregularities on the surface are minimized by an interpolation scheme and the initial coarse triangulation is refined by forcing nodes into equilibrium positions. A surface smoothing algorithm based on a low-pass filter is applied to remove sharp corners. The optimal number of iterations needed for polygon refinement and smoothing is determined by imposing a minimum average element quality index with no significant AAA sac volume change. This framework automatically generates high-quality triangular surface topologies that can be used to characterize local curvature changes of the AAA wall.
KeywordsAneurysm Surface topology Smoothing Optimal polygon quality Rupture Curvature
The authors would like to acknowledge research funding from the Bill and Melinda Gates Foundation, the John and Claire Bertucci Graduate Fellowship, Carnegie Mellon University’s Biomedical Engineering Department, and NIH Grants R21EB007651 and R21EB008804, from the National Institute of Biomedical Imaging and Bioengineering, and R15HL087268, from the National Heart, Lung, and Blood Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The contribution of Mr. Kyle Andrews in generating models for the code validation is greatly appreciated.
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