Direct estimation of wall shear stress from aneurysmal morphology: a statistical approach

Sarrami-Foroushani, A., Lassila, T., Pozo, J.M., Gooya, A. and Frangi, A.F. (2016) Direct estimation of wall shear stress from aneurysmal morphology: a statistical approach. In: Ourselin, S., Joskowicz, L., Sabuncu, M. R., Unal, G. and Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016. Series: Lecture notes in computer science (9902). Springer, pp. 201-209. ISBN 9783319467252 (doi: 10.1007/978-3-319-46726-9_24)

Abstract

Computational fluid dynamics (CFD) is a valuable tool for studying vascular diseases, but requires long computational time. To alleviate this issue, we propose a statistical framework to predict the aneurysmal wall shear stress patterns directly from the aneurysm shape. A database of 38 complex intracranial aneurysm shapes is used to generate aneurysm morphologies and CFD simulations. The shapes and wall shear stresses are then converted to clouds of hybrid points containing both types of information. These are subsequently used to train a joint statistical model implementing a mixture of principal component analyzers. Given a new aneurysmal shape, the trained joint model is firstly collapsed to a shape only model and used to initialize the missing shear stress values. The estimated hybrid point set is further refined by projection to the joint model space. We demonstrate that our predicted patterns can achieve significant similarities to the CFD-based results.

• Item Type: Book Sections
• Additional Information: eISBN: 9783319467269.
• Status: Published
• Glasgow Author(s) Enlighten ID: Gooya, Dr Ali
• Authors: Sarrami-Foroushani, A., Lassila, T., Pozo, J.M., Gooya, A., and Frangi, A.F.
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Springer
• ISBN: 9783319467252

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