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• W2892337987 abstract "Percutaneous coronary intervention (PCI) is the prevalent treatment for coronary artery disease, with hundreds of thousands of stents implanted annually. Computational studies have demonstrated the role of biomechanics in the failure of vascular stents, but clinical studies is this area are limited by a lack of understanding of the deployed stent geometry, which is required to accurately model and predict the stent-induced in vivo biomechanical environment. Herein, we present an automated method to reconstruct the 3-D deployed stent configuration through the fusion of optical coherence tomography (OCT) and micro-computed tomography ( μ CT) imaging data. In an experimental setup, OCT and μ CT data were collected in stents deployed in arterial phantoms ( n=4 ). A constrained iterative deformation process directed by diffeomorphic metric mapping was developed to deform μ CT data of a stent wireframe to the OCT-derived sparse point cloud of the deployed stent. Reconstructions of the deployed stents showed excellent agreement with the ground-truth configurations, with the distance between corresponding points on the reconstructed and ground-truth configurations of [Formula: see text]. Finally, reconstructions required <30 min of computational time. In conclusion, the developed and validated reconstruction algorithm provides a complete spatially resolved reconstruction of a deployed vascular stent from commercially available imaging modalities and has the potential, with further development, to provide more accurate computational models to evaluate the in vivo post-stent mechanical environment, as well as clinical visualization of the 3-D stent geometry immediately following PCI." @default.
• W2892337987 created "2018-09-27" @default.
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• W2892337987 date "2019-03-01" @default.
• W2892337987 modified "2023-10-16" @default.
• W2892337987 title "Establishment of an Automated Algorithm Utilizing Optical Coherence Tomography and Micro-Computed Tomography Imaging to Reconstruct the 3-D Deformed Stent Geometry" @default.
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• W2892337987 doi "https://doi.org/10.1109/tmi.2018.2870714" @default.
• W2892337987 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6407623" @default.
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