Accuracy of femur reconstruction from sparse geometric data using a statistical shape model.

Show simple item record Zhang, Ju en Besier, Thor en 2018-10-14T22:52:53Z en 2017-04 en
dc.identifier.issn 1025-5842 en
dc.identifier.uri en
dc.description.abstract Sparse geometric information from limited field-of-view medical images is often used to reconstruct the femur in biomechanical models of the hip and knee. However, the full femur geometry is needed to establish boundary conditions such as muscle attachment sites and joint axes which define the orientation of joint loads. Statistical shape models have been used to estimate the geometry of the full femur from varying amounts of sparse geometric information. However, the effect that different amounts of sparse data have on reconstruction accuracy has not been systematically assessed. In this study, we compared shape model and linear scaling reconstruction of the full femur surface from varying proportions of proximal and distal partial femur geometry in combination with morphometric and landmark data. We quantified reconstruction error in terms of surface-to-surface error as well as deviations in the reconstructed femur's anatomical coordinate system which is important for biomechanical models. Using a partial proximal femur surface, mean shape model-based reconstruction surface error was 1.8 mm with 0.15° or less anatomic axis error, compared to 19.1 mm and 2.7-5.6° for linear scaling. Similar results were found when using a partial distal surface. However, varying amounts of proximal or distal partial surface data had a negligible effect on reconstruction accuracy. Our results show that given an appropriate set of sparse geometric data, a shape model can reconstruct full femur geometry with far greater accuracy than simple scaling. en
dc.format.medium Print-Electronic en
dc.language eng en
dc.relation.ispartofseries Computer methods in biomechanics and biomedical engineering en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri en
dc.subject Femur en
dc.subject Humans en
dc.subject Models, Statistical en
dc.subject Image Processing, Computer-Assisted en
dc.subject Middle Aged en
dc.subject Female en
dc.subject Male en
dc.title Accuracy of femur reconstruction from sparse geometric data using a statistical shape model. en
dc.type Journal Article en
dc.identifier.doi 10.1080/10255842.2016.1263301 en
pubs.issue 5 en
pubs.begin-page 566 en
pubs.volume 20 en
dc.rights.holder Copyright: The author en
dc.identifier.pmid 27998170 en
pubs.end-page 576 en
pubs.publication-status Published en
dc.rights.accessrights en
pubs.subtype Journal Article en
pubs.elements-id 603746 en Bioengineering Institute en ABI Associates en
dc.identifier.eissn 1476-8259 en
pubs.record-created-at-source-date 2016-12-21 en
pubs.dimensions-id 27998170 en

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