Imperial College London

Angela Kedgley

Faculty of EngineeringDepartment of Bioengineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 0747a.kedgley Website

 
 
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Location

 

514BBuilding E - Sir Michael UrenWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Nolte:2020:10.1016/j.gaitpost.2020.02.010,
author = {Nolte, D and Ko, S-T and Bull, AMJ and Kedgley, AE},
doi = {10.1016/j.gaitpost.2020.02.010},
journal = {Gait & Posture},
pages = {269--275},
title = {Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling},
url = {http://dx.doi.org/10.1016/j.gaitpost.2020.02.010},
volume = {77},
year = {2020}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - BackgroundBone 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 questionThis 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.MethodsSSMs 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.ResultsSSM reconstructed surface predictions from varying landmark sets from skin and bone landmarks were more accurate compared to linear scaling methods (2.60–2.95mm vs. 3.66–3.87mm 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.SignificanceThese results indicate that SSM reconstructions can be used to increase the accurac
AU - Nolte,D
AU - Ko,S-T
AU - Bull,AMJ
AU - Kedgley,AE
DO - 10.1016/j.gaitpost.2020.02.010
EP - 275
PY - 2020///
SN - 0966-6362
SP - 269
TI - Reconstruction of the lower limb bones from digitised anatomical landmarks using statistical shape modelling
T2 - Gait & Posture
UR - http://dx.doi.org/10.1016/j.gaitpost.2020.02.010
UR - https://www.sciencedirect.com/science/article/abs/pii/S0966636220300710?via%3Dihub
UR - http://hdl.handle.net/10044/1/76896
VL - 77
ER -