Statistical finite element model for bone shape and biomechanical properties

Belenguer Querol, Laura; Büchler, Philippe; Rueckert, Daniel; Nolte, Lutz P.; González Ballester, Miguel Á. (2006). Statistical finite element model for bone shape and biomechanical properties. In: Larsen, Rasmus; Nielson, Mads; Sporring, Jon (eds.) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. Lecture Notes in Computer Science: Vol. 9 (pp. 405-411). Berlin: Springer 10.1007/11866565_50

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We present a framework for statistical finite element analysis combining shape and material properties, and allowing performing statistical statements of biomechanical performance across a given population. In this paper, we focus on the design of orthopaedic implants that fit a maximum percentage of the target population, both in terms of geometry and biomechanical stability. CT scans of the bone under consideration are registered non-rigidly to obtain correspondences in position and intensity between them. A statistical model of shape and intensity (bone density) is computed by means of principal component analysis. Afterwards, finite element analysis (FEA) is performed to analyse the biomechanical performance of the bones. Realistic forces are applied on the bones and the resulting displacement and bone stress distribution are calculated. The mechanical behaviour of different PCA bone instances is compared.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute for Surgical Technology & Biomechanics ISTB

UniBE Contributor:

Belenguer, Laura; Büchler, Philippe; Nolte, Lutz-Peter and Gonzalez Ballester, Miguel Angel

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

0302-9743

ISBN:

978-3-540-44708-5

Series:

Lecture Notes in Computer Science

Publisher:

Springer

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 14:49

Last Modified:

08 Jun 2016 10:40

Publisher DOI:

10.1007/11866565_50

PubMed ID:

17354916

Web of Science ID:

000241556300050

BORIS DOI:

10.7892/boris.20486

URI:

https://boris.unibe.ch/id/eprint/20486 (FactScience: 3958)

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