Abstract
Aortic diseases are one relevant cause of death in Western countries. They involve significant alterations of the aortic wall tissue, with consequent changes in the stiffness, i.e., the capability of the vessel to vary its section secondary to blood pressure variations. In this paper, we propose a Bayesian approach to estimate the aortic stiffness and its spatial variation, exploiting patient-specific geometrical data non-invasively derived from computed tomography angiography (CTA) images. The proposed method is tested considering a real clinical case, and outcomes show good estimates and the ability to detect local stiffness variations. The final objective is to support the adoption of imaging techniques such as the CTA as a standard tool for large-scale screening and early diagnosis of aortic diseases.
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Acknowledgements
The authors acknowledge Flagship Project “Factory of the Future Fab@Hospital”, funded by Italian CNR and MIUR organizations. Michele Conti acknowledges ERC Starting Grant through the Project ISOBIO: Isogeometric Methods for Biomechanics (No. 259229).
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Lanzarone, E., Auricchio, F., Conti, M., Ferrara, A. (2015). Bayesian Estimation of the Aortic Stiffness based on Non-invasive Computed Tomography Images. In: Frühwirth-Schnatter, S., Bitto, A., Kastner, G., Posekany, A. (eds) Bayesian Statistics from Methods to Models and Applications. Springer Proceedings in Mathematics & Statistics, vol 126. Springer, Cham. https://doi.org/10.1007/978-3-319-16238-6_12
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DOI: https://doi.org/10.1007/978-3-319-16238-6_12
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