Sarrami-Foroushani, A., Lassila, T. orcid.org/0000-0001-8947-1447, Pozo Soler, J.M. et al. (2 more authors) (2016) Direct estimation of wall shear stress from aneurysmal morphology: a statistical approach. In: Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016 Proceedings, Part III. 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2016), 17-21 Oct 2016, Athens, Greece. , pp. 201-209.
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.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Springer. This is an author produced version of a paper subsequently published in Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016 Proceedings part III. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Sep 2016 15:13 |
Last Modified: | 03 Oct 2017 01:09 |
Published Version: | https://dx.doi.org/10.1007/978-3-319-46726-9_24 |
Status: | Published |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:105101 |