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• W2765147435 abstract "We present novel numerical methods for polyline-to-point-cloud registration and their application to patient-specific modeling of deployed coronary artery stents from image data. Patient-specific coronary stent reconstruction is an important challenge in computational hemodynamics and relevant to the design and improvement of the prostheses. It is an invaluable tool in large-scale clinical trials that computationally investigate the effect of new generations of stents on hemodynamics and eventually tissue remodeling. Given a point cloud of strut positions, which can be extracted from images, our stent reconstruction method aims at finding a geometrical transformation that aligns a model of the undeployed stent to the point cloud. Mathematically, we describe the undeployed stent as a polyline, which is a piecewise linear object defined by its vertices and edges. We formulate the nonlinear registration as an optimization problem whose objective function consists of a similarity measure, quantifying the distance between the polyline and the point cloud, and a regularization functional, penalizing undesired transformations. Using projections of points onto the polyline structure, we derive novel distance measures. Our formulation supports most commonly used transformation models including very flexible nonlinear deformations. We also propose 2 regularization approaches ensuring the smoothness of the estimated nonlinear transformation. We demonstrate the potential of our methods using an academic 2D example and a real-life 3D bioabsorbable stent reconstruction problem. Our results show that the registration problem can be solved to sufficient accuracy within seconds using only a few number of Gauss-Newton iterations." @default.
• W2765147435 created "2017-11-10" @default.
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• W2765147435 date "2017-11-29" @default.
• W2765147435 modified "2023-09-23" @default.
• W2765147435 title "Numerical methods for polyline-to-point-cloud registration with applications to patient-specific stent reconstruction" @default.
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• W2765147435 doi "https://doi.org/10.1002/cnm.2934" @default.
• W2765147435 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29073332" @default.
• W2765147435 hasPublicationYear "2017" @default.
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