Comprehensive evaluation of PCA-based finite element modelling of the human femur
Grassi, Lorenzo LU
; Schileo, Enrico
; Boichon, Christelle
; Viceconti, Marco
and Taddei, Fulvia
(2014)
In Medical Engineering & Physics
- Abstract
- Computed tomography (CT)-based finite element (FE) reconstructions describe shape and density distribution of bones. Both shape and density distribution, however, can vary a lot between individuals. Shape/density indexation (usually achieved by principal component analysis—PCA) can be used to synthesize realistic models, thus overcoming the shortage of CT-based models, and helping e.g. to study fracture determinants, or steer prostheses design. The aim of this study was to describe a PCA-based statistical modelling algorithm, and test it on a large CT-based population of femora, to see if it can accurately describe and reproduce bone shape, density distribution, and biomechanics.
To this aim, 115 CT-datasets showing... (More) - Computed tomography (CT)-based finite element (FE) reconstructions describe shape and density distribution of bones. Both shape and density distribution, however, can vary a lot between individuals. Shape/density indexation (usually achieved by principal component analysis—PCA) can be used to synthesize realistic models, thus overcoming the shortage of CT-based models, and helping e.g. to study fracture determinants, or steer prostheses design. The aim of this study was to describe a PCA-based statistical modelling algorithm, and test it on a large CT-based population of femora, to see if it can accurately describe and reproduce bone shape, density distribution, and biomechanics.
To this aim, 115 CT-datasets showing normal femoral anatomy were collected and characterized. Isotopological FE meshes were built. Shape and density indexation procedures were performed on the mesh database. The completeness of the database was evaluated through a convergence study. The accuracy in reconstructing bones not belonging to the indexation database was evaluated through (i) leave-one-out tests (ii) comparison of calculated vs. in-vitro measured strains.
Fifty indexation modes for shape and 40 for density were necessary to achieve reconstruction errors below pixel size for shape, and below 10% for density. Similar errors for density, and slightly higher errors for shape were obtained when reconstructing bones not belonging to the database. The in-vitro strain prediction accuracy of the reconstructed FE models was comparable to state-of-the-art studies.
In summary, the results indicate that the proposed statistical modelling tools are able to accurately describe a population of femora through finite element models. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4588999
- author
- Grassi, Lorenzo
LU
; Schileo, Enrico
; Boichon, Christelle
; Viceconti, Marco
and Taddei, Fulvia
- publishing date
- 2014
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Statistical shape modelling, Femur, Bone biomechanics, Principal component analysis
- in
- Medical Engineering & Physics
- publisher
- Elsevier
- external identifiers
-
- scopus:84908027656
- pmid:25128959
- ISSN
- 1873-4030
- DOI
- 10.1016/j.medengphy.2014.06.021
- language
- English
- LU publication?
- no
- id
- f05e72f8-c373-4bfc-aa79-945cbb6b196f (old id 4588999)
- alternative location
- http://www.sciencedirect.com/science/article/pii/S1350453314001805#
- date added to LUP
- 2016-04-04 09:39:22
- date last changed
- 2022-01-29 18:54:14
@article{f05e72f8-c373-4bfc-aa79-945cbb6b196f,
abstract = {{Computed tomography (CT)-based finite element (FE) reconstructions describe shape and density distribution of bones. Both shape and density distribution, however, can vary a lot between individuals. Shape/density indexation (usually achieved by principal component analysis—PCA) can be used to synthesize realistic models, thus overcoming the shortage of CT-based models, and helping e.g. to study fracture determinants, or steer prostheses design. The aim of this study was to describe a PCA-based statistical modelling algorithm, and test it on a large CT-based population of femora, to see if it can accurately describe and reproduce bone shape, density distribution, and biomechanics.<br/><br>
<br/><br>
To this aim, 115 CT-datasets showing normal femoral anatomy were collected and characterized. Isotopological FE meshes were built. Shape and density indexation procedures were performed on the mesh database. The completeness of the database was evaluated through a convergence study. The accuracy in reconstructing bones not belonging to the indexation database was evaluated through (i) leave-one-out tests (ii) comparison of calculated vs. in-vitro measured strains.<br/><br>
<br/><br>
Fifty indexation modes for shape and 40 for density were necessary to achieve reconstruction errors below pixel size for shape, and below 10% for density. Similar errors for density, and slightly higher errors for shape were obtained when reconstructing bones not belonging to the database. The in-vitro strain prediction accuracy of the reconstructed FE models was comparable to state-of-the-art studies.<br/><br>
<br/><br>
In summary, the results indicate that the proposed statistical modelling tools are able to accurately describe a population of femora through finite element models.}},
author = {{Grassi, Lorenzo and Schileo, Enrico and Boichon, Christelle and Viceconti, Marco and Taddei, Fulvia}},
issn = {{1873-4030}},
keywords = {{Statistical shape modelling; Femur; Bone biomechanics; Principal component analysis}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Medical Engineering & Physics}},
title = {{Comprehensive evaluation of PCA-based finite element modelling of the human femur}},
url = {{http://dx.doi.org/10.1016/j.medengphy.2014.06.021}},
doi = {{10.1016/j.medengphy.2014.06.021}},
year = {{2014}},
}