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High resolution cortical bone thickness measurement from clinical CT data.

Treece, GM and Gee, AH and Mayhew, PM and Poole, KE (2010) High resolution cortical bone thickness measurement from clinical CT data. Med Image Anal, 14. pp. 276-290.

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Abstract

The distribution of cortical bone in the proximal femur is believed to be a critical component in determining fracture resistance. Current CT technology is limited in its ability to measure cortical thickness, especially in the sub-millimetre range which lies within the point spread function of today's clinical scanners. In this paper, we present a novel technique that is capable of producing unbiased thickness estimates down to 0.3mm. The technique relies on a mathematical model of the anatomy and the imaging system, which is fitted to the data at a large number of sites around the proximal femur, producing around 17,000 independent thickness estimates per specimen. In a series of experiments on 16 cadaveric femurs, estimation errors were measured as -0.01+/-0.58mm (mean+/-1std.dev.) for cortical thicknesses in the range 0.3-4mm. This compares with 0.25+/-0.69mm for simple thresholding and 0.90+/-0.92mm for a variant of the 50% relative threshold method. In the clinically relevant sub-millimetre range, thresholding increasingly fails to detect the cortex at all, whereas the new technique continues to perform well. The many cortical thickness estimates can be displayed as a colour map painted onto the femoral surface. Computation of the surfaces and colour maps is largely automatic, requiring around 15min on a modest laptop computer.

Item Type: Article
Uncontrolled Keywords: Adult Aged, 80 and over Algorithms Bone Density Computer Simulation Female Femoral Fractures Femur Humans Imaging, Three-Dimensional Male Models, Biological Pattern Recognition, Automated Radiographic Image Enhancement Radiographic Image Interpretation, Computer-Assisted Reproducibility of Results Sensitivity and Specificity Tomography, X-Ray Computed
Subjects: UNSPECIFIED
Divisions: Div F > Machine Intelligence
Depositing User: Cron Job
Date Deposited: 07 Mar 2014 11:22
Last Modified: 18 Apr 2014 19:01
DOI: 10.1016/j.media.2010.01.003

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