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Proceedings Paper

Cortical thickness estimation of the proximal femur from multi-view dual-energy X-ray absorptiometry (DXA)
Author(s): N. Tsaousis; A. H. Gee; G. M. Treece; K.E.S. Poole
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Paper Abstract

Hip fracture is the leading cause of acute orthopaedic hospital admission amongst the elderly, with around a third of patients not surviving one year post-fracture. Although various preventative therapies are available, patient selection is difficult. The current state-of-the-art risk assessment tool (FRAX) ignores focal structural defects, such as cortical bone thinning, a critical component in characterizing hip fragility. Cortical thickness can be measured using CT, but this is expensive and involves a significant radiation dose. Instead, Dual-Energy X-ray Absorptiometry (DXA) is currently the preferred imaging modality for assessing hip fracture risk and is used routinely in clinical practice. Our ambition is to develop a tool to measure cortical thickness using multi-view DXA instead of CT. In this initial study, we work with digitally reconstructed radiographs (DRRs) derived from CT data as a surrogate for DXA scans: this enables us to compare directly the thickness estimates with the gold standard CT results. Our approach involves a model-based femoral shape reconstruction followed by a data-driven algorithm to extract numerous cortical thickness point estimates. In a series of experiments on the shaft and trochanteric regions of 48 proximal femurs, we validated our algorithm and established its performance limits using 20 views in the range 0°–171°: estimation errors were 0:19 ± 0:53mm (mean +/- one standard deviation). In a more clinically viable protocol using four views in the range 0°–51°, where no other bony structures obstruct the projection of the femur, measurement errors were −0:07 ± 0:79 mm.

Paper Details

Date Published: 28 February 2013
PDF: 9 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86700B (28 February 2013); doi: 10.1117/12.2006389
Show Author Affiliations
N. Tsaousis, Univ. of Cambridge (United Kingdom)
A. H. Gee, Univ. of Cambridge (United Kingdom)
G. M. Treece, Univ. of Cambridge (United Kingdom)
K.E.S. Poole, Univ. of Cambridge (United Kingdom)

Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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