Statistical modelling of the whole human femur incorporating geometric and material properties
Statistical modelling of the whole human femur incorporating geometric and material properties
When analysing the performance of orthopaedic implants the vast majority of computational studies use either a single or limited number of bone models. The results are then extrapolated to the population as a whole, overlooking the inherent and large interpatient variability in bone quality and geometry. This paper describes the creation of a three dimensional, statistical, finite element analysis (FEA) ready model of the femur using principal component analysis. To achieve this a registration scheme based on elastic surface matching and a mesh morphing algorithm has been developed. This method is fully automated enabling registration and generation of high resolution models. The variation in both geometry and material properties was extracted from 46 computer tomography scans and captured by the statistical model. Analysis of mesh quality showed this was maintained throughout the model generation and sampling process. Reconstruction of the training femurs showed 35 eigenmodes were required for accurate reproduction. A set of unique, anatomically realistic femur models were generated using the statistical model, with a variation comparable to that seen in the population. This study illustrates a methodology with the potential to generate femur models incorporating material properties for large scale multi-femur finite element studies
statistical model, principal component analysis, registration, femur, material property
57-65
Bryan, Rebecca
58870a3c-49f9-4473-8ba6-6518e4fa5328
Mohan, P. Surya
0622f14f-3ee9-457b-9153-c0fdfbb1dc91
Hopkins, Andrew
15a0bd01-47d7-421b-b894-d24e1d62a4d0
Galloway, Francis
9efdb46e-a0b9-4454-b28f-493c49bf7b14
Taylor, Mark
e368bda3-6ca5-4178-80e9-41a689badeeb
Nair, Prasanth B.
d4d61705-bc97-478e-9e11-bcef6683afe7
January 2010
Bryan, Rebecca
58870a3c-49f9-4473-8ba6-6518e4fa5328
Mohan, P. Surya
0622f14f-3ee9-457b-9153-c0fdfbb1dc91
Hopkins, Andrew
15a0bd01-47d7-421b-b894-d24e1d62a4d0
Galloway, Francis
9efdb46e-a0b9-4454-b28f-493c49bf7b14
Taylor, Mark
e368bda3-6ca5-4178-80e9-41a689badeeb
Nair, Prasanth B.
d4d61705-bc97-478e-9e11-bcef6683afe7
Bryan, Rebecca, Mohan, P. Surya, Hopkins, Andrew, Galloway, Francis, Taylor, Mark and Nair, Prasanth B.
(2010)
Statistical modelling of the whole human femur incorporating geometric and material properties.
Medical Engineering & Physics, 32 (1), .
(doi:10.1016/j.medengphy.2009.10.008).
(PMID:19932044)
Abstract
When analysing the performance of orthopaedic implants the vast majority of computational studies use either a single or limited number of bone models. The results are then extrapolated to the population as a whole, overlooking the inherent and large interpatient variability in bone quality and geometry. This paper describes the creation of a three dimensional, statistical, finite element analysis (FEA) ready model of the femur using principal component analysis. To achieve this a registration scheme based on elastic surface matching and a mesh morphing algorithm has been developed. This method is fully automated enabling registration and generation of high resolution models. The variation in both geometry and material properties was extracted from 46 computer tomography scans and captured by the statistical model. Analysis of mesh quality showed this was maintained throughout the model generation and sampling process. Reconstruction of the training femurs showed 35 eigenmodes were required for accurate reproduction. A set of unique, anatomically realistic femur models were generated using the statistical model, with a variation comparable to that seen in the population. This study illustrates a methodology with the potential to generate femur models incorporating material properties for large scale multi-femur finite element studies
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Published date: January 2010
Keywords:
statistical model, principal component analysis, registration, femur, material property
Identifiers
Local EPrints ID: 143551
URI: http://eprints.soton.ac.uk/id/eprint/143551
ISSN: 1350-4533
PURE UUID: 298fb895-a78d-4be3-9480-b390570d40bb
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Date deposited: 12 Apr 2010 10:59
Last modified: 14 Mar 2024 00:43
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Contributors
Author:
Rebecca Bryan
Author:
P. Surya Mohan
Author:
Andrew Hopkins
Author:
Francis Galloway
Author:
Mark Taylor
Author:
Prasanth B. Nair
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