Abstract
One of the techniques used to diagnose carotid artery disease is the ultrasound (US) examination. The initiation and development of vascular diseases depends also on the flow conditions in the artery. Additional parameters that cannot be directly measured can be obtained by performing numerical simulations using patient-specific geometry. In this study, the Finite Element Method (FEM) is used to analyze the distribution of relevant blood flow characteristics. Images obtained from the US examinations are used to adapt the generalized carotid bifurcation model to the specific patient. The approach presented in this study combines the deep learning approach for the image segmentation and automated 3D reconstruction method to create a semi-generic geometrical model of the carotid artery that is adapted to the specific patient, using data obtained from only several US images. The presented methodology enables efficient segmentation, extraction of the morphological parameters and creation of 3D meshed volume models that can be also used for the further computational simulations of blood flow.
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Acknowledgements
The research presented in this study was part of the project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 755320-2 - TAXINOMISIS. This article reflects only the author's view. The Commission is not responsible for any use that may be made of the information it contains. The research is also supported by the Ministry of Education, Science and Technological Development of the Republic of Serbia (project numbers III41007 and ON174028).
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Djukic, T., Arsic, B., Koncar, I., Filipovic, N. (2021). 3D Reconstruction of Patient-Specific Carotid Artery Geometry Using Clinical Ultrasound Imaging. In: Miller, K., Wittek, A., Nash, M., Nielsen, P.M.F. (eds) Computational Biomechanics for Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-70123-9_6
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