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dc.contributor.authorVäänänen, Sami P.
dc.contributor.authorGrassi, Lorenzo
dc.contributor.authorFlivik, Gunnar
dc.contributor.authorJurvelin, Jukka S.
dc.contributor.authorIsaksson, Hanna
dc.date.accessioned2016-06-29T05:41:30Z
dc.date.available2016-06-29T05:41:30Z
dc.date.issued2015-06-19
dc.identifier10.1016/j.media.2015.06.001
dc.identifier.citationVaananen et al., 2015 S.P. Vaananen, L. Grassi, G. Flivik, J.S. Jurvelin, H. Isaksson Generation of 3D shape, density, cortical thickness and finite element mesh of proximal femur from a DXA imagefi_FI
dc.identifier.issn1361-8415
dc.identifier.urihttps://erepo.uef.fi/handle/123456789/104
dc.descriptionArticle
dc.description.abstractAreal 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 (set 1, 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 1.0 mm for cadaver femurs in set 1 (leave-one-out test) and 1.4 mm for clinical subjects in set 3. The reconstructed volumetric BMD showed a mean absolute difference of 140 and 185 mg/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.fi_FI
dc.language.isoenfi_FI
dc.publisherElsevier BVfi_FI
dc.relation.ispartofseriesMedical Image Analysis 24 (1);
dc.relation.urihttp://dx.doi.org/10.1016/j.media.2015.06.001
dc.rightsAll rights reserved
dc.subjectShape reconstructionfi_FI
dc.subjectFinite elementfi_FI
dc.subjectProximal femurfi_FI
dc.subjectDXAfi_FI
dc.subjectBone mineral densityfi_FI
dc.subjectStatistical appearance modelfi_FI
dc.titleGeneration of 3D shape, density, cortical thickness and finite element mesh of proximal femur from a DXA imagefi_FI
dc.typehttp://purl.org/eprint/type/JournalArticle
dc.description.versionFinal Draft
dc.contributor.departmentFaculty of Science and Forestry
uef.solecris.id33966717
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.type.publicationinfo:eu-repo/semantics/article
dc.rights.accessrights© Elsevier B.V.
dc.relation.doi10.1016/j.media.2015.06.001
dc.description.reviewstatushttp://purl.org/eprint/status/PeerReviewed
dc.relation.issn1361-8415
dc.rights.accesslevelopenAccess


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