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Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling

BACKGROUND: Bone shapes strongly influence force and moment predictions of kinematic and musculoskeletal models used in motion analysis. The precise determination of joint reference frames is essential for accurate predictions. Since clinical motion analysis typically does not include medical imagin...

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Autores principales: Nolte, Daniel, Ko, Siu-Teing, Bull, Anthony M.J., Kedgley, Angela E.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Sciencem 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7090904/
https://www.ncbi.nlm.nih.gov/pubmed/32092603
http://dx.doi.org/10.1016/j.gaitpost.2020.02.010
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author Nolte, Daniel
Ko, Siu-Teing
Bull, Anthony M.J.
Kedgley, Angela E.
author_facet Nolte, Daniel
Ko, Siu-Teing
Bull, Anthony M.J.
Kedgley, Angela E.
author_sort Nolte, Daniel
collection PubMed
description BACKGROUND: Bone shapes strongly influence force and moment predictions of kinematic and musculoskeletal models used in motion analysis. The precise determination of joint reference frames is essential for accurate predictions. Since clinical motion analysis typically does not include medical imaging, from which bone shapes may be obtained, scaling methods using reference subjects to create subject-specific bone geometries are widely used. RESEARCH QUESTION: This study investigated if lower limb bone shape predictions from skin-based measurements, utilising an underlying statistical shape model (SSM) that corrects for soft tissue artefacts in digitisation, can be used to improve conventional linear scaling methods of bone geometries. METHODS: SSMs created from 35 healthy adult femurs and tibiae/fibulae were used to reconstruct bone shapes by minimising the distance between anatomical landmarks on the models and those digitised in the motion laboratory or on medical images. Soft tissue artefacts were quantified from magnetic resonance images and then used to predict distances between landmarks digitised on the skin surface and bone. Reconstruction results were compared to linearly scaled models by measuring root mean squared distances to segmented surfaces, calculating differences of commonly used anatomical measures and the errors in the prediction of the hip joint centre. RESULTS: SSM reconstructed surface predictions from varying landmark sets from skin and bone landmarks were more accurate compared to linear scaling methods (2.60–2.95 mm vs. 3.66–3.87 mm median error; p < 0.05). No significant differences were found between SSM reconstructions from bony landmarks and SSM reconstructions from digitised landmarks obtained in the motion lab and therefore reconstructions using skin landmarks are as accurate as reconstructions from landmarks obtained from medical images. SIGNIFICANCE: These results indicate that SSM reconstructions can be used to increase the accuracy in obtaining bone shapes from surface digitised experimental data acquired in motion lab environments.
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spelling pubmed-70909042020-03-27 Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling Nolte, Daniel Ko, Siu-Teing Bull, Anthony M.J. Kedgley, Angela E. Gait Posture Article BACKGROUND: Bone shapes strongly influence force and moment predictions of kinematic and musculoskeletal models used in motion analysis. The precise determination of joint reference frames is essential for accurate predictions. Since clinical motion analysis typically does not include medical imaging, from which bone shapes may be obtained, scaling methods using reference subjects to create subject-specific bone geometries are widely used. RESEARCH QUESTION: This study investigated if lower limb bone shape predictions from skin-based measurements, utilising an underlying statistical shape model (SSM) that corrects for soft tissue artefacts in digitisation, can be used to improve conventional linear scaling methods of bone geometries. METHODS: SSMs created from 35 healthy adult femurs and tibiae/fibulae were used to reconstruct bone shapes by minimising the distance between anatomical landmarks on the models and those digitised in the motion laboratory or on medical images. Soft tissue artefacts were quantified from magnetic resonance images and then used to predict distances between landmarks digitised on the skin surface and bone. Reconstruction results were compared to linearly scaled models by measuring root mean squared distances to segmented surfaces, calculating differences of commonly used anatomical measures and the errors in the prediction of the hip joint centre. RESULTS: SSM reconstructed surface predictions from varying landmark sets from skin and bone landmarks were more accurate compared to linear scaling methods (2.60–2.95 mm vs. 3.66–3.87 mm median error; p < 0.05). No significant differences were found between SSM reconstructions from bony landmarks and SSM reconstructions from digitised landmarks obtained in the motion lab and therefore reconstructions using skin landmarks are as accurate as reconstructions from landmarks obtained from medical images. SIGNIFICANCE: These results indicate that SSM reconstructions can be used to increase the accuracy in obtaining bone shapes from surface digitised experimental data acquired in motion lab environments. Elsevier Sciencem 2020-03 /pmc/articles/PMC7090904/ /pubmed/32092603 http://dx.doi.org/10.1016/j.gaitpost.2020.02.010 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Nolte, Daniel
Ko, Siu-Teing
Bull, Anthony M.J.
Kedgley, Angela E.
Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling
title Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling
title_full Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling
title_fullStr Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling
title_full_unstemmed Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling
title_short Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling
title_sort reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7090904/
https://www.ncbi.nlm.nih.gov/pubmed/32092603
http://dx.doi.org/10.1016/j.gaitpost.2020.02.010
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