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Generation of 3D shape, density, cortical thickness and finite element mesh of proximal femur from a DXA image

Väänänen, Sami P; Grassi, Lorenzo LU ; Flivik, Gunnar LU ; Jurvelin, Jukka S and Isaksson, Hanna LU (2015) In Medical Image Analysis 24(1). p.125-134
Abstract
Areal bone mineral density (aBMD), as measured by dual-energy X-ray absorptiometry (DXA), predicts hip fracture risk only moderately. Simulation of bone mechanics based on DXA imaging of the proximal femur, may help to improve the prediction accuracy. Therefore, we collected three (1-3) image sets, including CT images and DXA images of 34 proximal cadaver femurs (set1, including 30 males, 4 females), 35 clinical patient CT images of the hip (set 2, including 27 males, 8 females) and both CT and DXA images of clinical patients (set 3, including 12 female patients). All CT images were segmented manually and landmarks were placed on both femurs and pelvises. Two separate statistical appearance models (SAMs) were built using the CT images of... (More)
Areal bone mineral density (aBMD), as measured by dual-energy X-ray absorptiometry (DXA), predicts hip fracture risk only moderately. Simulation of bone mechanics based on DXA imaging of the proximal femur, may help to improve the prediction accuracy. Therefore, we collected three (1-3) image sets, including CT images and DXA images of 34 proximal cadaver femurs (set1, including 30 males, 4 females), 35 clinical patient CT images of the hip (set 2, including 27 males, 8 females) and both CT and DXA images of clinical patients (set 3, including 12 female patients). All CT images were segmented manually and landmarks were placed on both femurs and pelvises. Two separate statistical appearance models (SAMs) were built using the CT images of the femurs and pelvises in sets 1 and 2, respectively. The 3D shape of the femur was reconstructed from the DXA image by matching the SAMs with the DXA images. The orientation and modes of variation of the SAMs were adjusted to minimize the sum of the absolute differences between the projection of the SAMs and a DXA image. The mesh quality and the location of the SAMs with respect to the manually placed control points on the DXA image were used as additional constraints. Then, finite element (FE) models were built from the reconstructed shapes. Mean point-to-surface distance between the reconstructed shape and CT image was 1.0mm for cadaver femurs in set 1 (leave-one-out test) and 1.4mm for clinical subjects in set 3. The reconstructed volumetric BMD showed a mean absolute difference of 140 and 185mg/cm3 for set 1 and set 3 respectively. The generation of the SAM and the limitation of using only one 2D image were found to be the most significant sources of errors in the shape reconstruction. The noise in the DXA images had only small effect on the accuracy of the shape reconstruction. DXA-based FE simulation was able to explain 85% of the CT-predicted strength of the femur in stance loading. The present method can be used to accurately reconstruct the 3D shape and internal density of the femur from 2D DXA images. This may help to derive new information from clinical DXA images by producing patient-specific FE models for mechanical simulation of femoral bone mechanics. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Shape reconstruction, Finite element, Proximal femur, DXA, Bone mineral density, Statistical appearance model
in
Medical Image Analysis
volume
24
issue
1
pages
125 - 134
publisher
Elsevier
external identifiers
  • pmid:26148575
  • wos:000360252700010
  • scopus:84935498365
ISSN
1361-8415
DOI
10.1016/j.media.2015.06.001
language
English
LU publication?
yes
id
d7b725e0-9fb7-4876-9d16-e5ed75af52fc (old id 7374321)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/26148575?dopt=Abstract
date added to LUP
2015-06-22 14:01:12
date last changed
2017-11-05 03:03:44
@article{d7b725e0-9fb7-4876-9d16-e5ed75af52fc,
  abstract     = {Areal bone mineral density (aBMD), as measured by dual-energy X-ray absorptiometry (DXA), predicts hip fracture risk only moderately. Simulation of bone mechanics based on DXA imaging of the proximal femur, may help to improve the prediction accuracy. Therefore, we collected three (1-3) image sets, including CT images and DXA images of 34 proximal cadaver femurs (set1, including 30 males, 4 females), 35 clinical patient CT images of the hip (set 2, including 27 males, 8 females) and both CT and DXA images of clinical patients (set 3, including 12 female patients). All CT images were segmented manually and landmarks were placed on both femurs and pelvises. Two separate statistical appearance models (SAMs) were built using the CT images of the femurs and pelvises in sets 1 and 2, respectively. The 3D shape of the femur was reconstructed from the DXA image by matching the SAMs with the DXA images. The orientation and modes of variation of the SAMs were adjusted to minimize the sum of the absolute differences between the projection of the SAMs and a DXA image. The mesh quality and the location of the SAMs with respect to the manually placed control points on the DXA image were used as additional constraints. Then, finite element (FE) models were built from the reconstructed shapes. Mean point-to-surface distance between the reconstructed shape and CT image was 1.0mm for cadaver femurs in set 1 (leave-one-out test) and 1.4mm for clinical subjects in set 3. The reconstructed volumetric BMD showed a mean absolute difference of 140 and 185mg/cm3 for set 1 and set 3 respectively. The generation of the SAM and the limitation of using only one 2D image were found to be the most significant sources of errors in the shape reconstruction. The noise in the DXA images had only small effect on the accuracy of the shape reconstruction. DXA-based FE simulation was able to explain 85% of the CT-predicted strength of the femur in stance loading. The present method can be used to accurately reconstruct the 3D shape and internal density of the femur from 2D DXA images. This may help to derive new information from clinical DXA images by producing patient-specific FE models for mechanical simulation of femoral bone mechanics.},
  author       = {Väänänen, Sami P and Grassi, Lorenzo and Flivik, Gunnar and Jurvelin, Jukka S and Isaksson, Hanna},
  issn         = {1361-8415},
  keyword      = {Shape reconstruction,Finite element,Proximal femur,DXA,Bone mineral density,Statistical appearance model},
  language     = {eng},
  number       = {1},
  pages        = {125--134},
  publisher    = {Elsevier},
  series       = {Medical Image Analysis},
  title        = {Generation of 3D shape, density, cortical thickness and finite element mesh of proximal femur from a DXA image},
  url          = {http://dx.doi.org/10.1016/j.media.2015.06.001},
  volume       = {24},
  year         = {2015},
}