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Direct Estimation of Wall Shear Stress from Aneurysmal Morphology: A Statistical Approach

  • Ali Sarrami-Foroushani
  • Toni Lassila
  • Jose M. Pozo
  • Ali Gooya
  • Alejandro F. FrangiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)

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.

Notes

Acknowledgements

This project was partly supported by the Marie Curie Individual Fellowship (625745, A. Gooya). The aneurysm dataset has been provided by the European integrated project @neurIST (IST-027703).

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© Springer International Publishing AG 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Ali Sarrami-Foroushani
    • 1
  • Toni Lassila
    • 1
  • Jose M. Pozo
    • 1
  • Ali Gooya
    • 1
  • Alejandro F. Frangi
    • 1
    Email author
  1. 1.Department of Electronic and Electrical Engineering, Centre for Computational Imaging and Simulation Technologies in BiomedicineThe University of SheffieldSheffieldUK

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