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J Biomech. 2010 May 28;43(8):1463-9. doi: 10.1016/j.jbiomech.2010.02.006. Epub 2010 Feb 24.

Statistical descriptions of scaphoid and lunate bone shapes.

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  • 1Delft University of Technology, Quantitative Imaging, Lorentzweg 1, 2628 CJ Delft, The Netherlands. m.vandegiessen@tudelft.nl

Abstract

Diagnosing of injuries of the wrist bones is problematic due to a highly complex and variable geometry. knowledge of variations of healthy bone shapes is essential to detect wrist pathologies, developing prosthetics and investigating biomechanical properties of the wrist joint. In previous literature various methods have been proposed to classify different scaphoid and lunate types. These classifications were mainly qualitative or were based on a limited number of manually determined surface points. The purposes of this study are to develop a quantitative, standardized description of the variations in the scaphoid and lunate and to investigate whether it is feasible to divide carpal bones in isolated shape categories based on statistical grounds. The shape variations of the scaphoid and lunate were described by constructing a statistical shape model (SSM) of healthy bones. SSM shape parameters were determined that describe the deviation of each shape from the mean shape. The first five modes of variation in the SSMs describe 60% of the total variance of the scaphoid and 57% of the lunate. Higher modes describe less than 5% of the variance per mode. The distributions of the parameters that characterize the bone shape variations along the modes do not significantly differ from a normal distribution. The SSM provides a description of possible shape variations and the distribution of scaphoid and lunate shapes in our population at an accuracy of approximately the voxel size (0.3x0.3x0.3mm(3)). The developed statistical shape model represents the previously qualitatively described variations of scaphoid and lunate. However, strict classifications based on shape differences are not feasible on statistical grounds.

Copyright 2010 Elsevier Ltd. All rights reserved.

PMID:
20185138
[PubMed - indexed for MEDLINE]
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