INTRODUCTION: Fractures of the proximal humerus represent a major osteoporotic burden. Recent developments in CT imaging have emphasized the importance of cortical bone thickness distribution in the prevention and management of fragility fractures. We aimed to experimentally define the CT density of cortical bone in the proximal humerus for building cortical geometry maps. METHODS: With ethical approval, we used ten fresh-frozen human proximal humeri. These were stripped of all soft tissue and high-resolution CT images were then taken. The humeral heads were then subsequently resected to allow access to the metaphyseal area. Using curettes, cancellous bone was removed down to hard cortical bone. Another set of CT images of the reamed specimen was then taken. Using CT imaging software and a CAD interface, we then compared cortical contours at different CT density thresholds to the reference inner cortical contour of our reamed specimens. Working with 3D model representations of these cortical maps, we were able to accurately make distance comparison analyses based on different CT thresholds. RESULTS: We could compute a single closest value at 700 HU. No difference was found in the HU-based contours generated along the 500-900 HU pixels (p = 1.000). The contours were significantly different from those generated at 300, 400, 1,000, and 1,100 HU. CONCLUSIONS: A Hounsfield range of 500-900 HU can accurately depict cortical bone geometry in the proximal humerus. Thresholding outside this range leads to statistically significant inaccuracies. Our results concur with a similar range reported in the literature for the proximal femur. Knowledge of regional variations in cortical bone thickness has direct implications for basic science studies on osteoporosis and its treatment, but is also important for the orthopedic surgeon since our decision for treatment options is often guided by local bone quality.
INTRODUCTION: Fractures of the proximal humerus represent a major osteoporotic burden. Recent developments in CT imaging have emphasized the importance of cortical bone thickness distribution in the prevention and management of fragility fractures. We aimed to experimentally define the CT density of cortical bone in the proximal humerus for building cortical geometry maps. METHODS: With ethical approval, we used ten fresh-frozen human proximal humeri. These were stripped of all soft tissue and high-resolution CT images were then taken. The humeral heads were then subsequently resected to allow access to the metaphyseal area. Using curettes, cancellous bone was removed down to hard cortical bone. Another set of CT images of the reamed specimen was then taken. Using CT imaging software and a CAD interface, we then compared cortical contours at different CT density thresholds to the reference inner cortical contour of our reamed specimens. Working with 3D model representations of these cortical maps, we were able to accurately make distance comparison analyses based on different CT thresholds. RESULTS: We could compute a single closest value at 700 HU. No difference was found in the HU-based contours generated along the 500-900 HU pixels (p = 1.000). The contours were significantly different from those generated at 300, 400, 1,000, and 1,100 HU. CONCLUSIONS: A Hounsfield range of 500-900 HU can accurately depict cortical bone geometry in the proximal humerus. Thresholding outside this range leads to statistically significant inaccuracies. Our results concur with a similar range reported in the literature for the proximal femur. Knowledge of regional variations in cortical bone thickness has direct implications for basic science studies on osteoporosis and its treatment, but is also important for the orthopedic surgeon since our decision for treatment options is often guided by local bone quality.
Authors: Shane J Nho; Robert H Brophy; Joseph U Barker; Charles N Cornell; John D MacGillivray Journal: J Am Acad Orthop Surg Date: 2007-01 Impact factor: 3.020
Authors: Paul M Mayhew; C David Thomas; John G Clement; Nigel Loveridge; Thomas J Beck; William Bonfield; Chris J Burgoyne; Jonathan Reeve Journal: Lancet Date: 2005 Jul 9-15 Impact factor: 79.321
Authors: Peter M de Bakker; Sarah L Manske; Vincent Ebacher; Thomas R Oxland; Peter A Cripton; Pierre Guy Journal: J Biomech Date: 2009-06-13 Impact factor: 2.712
Authors: Johannes Christof Hopf; Andreas Jähnig; Tobias Jorg; Ruben Sebastian Westphal; Daniel Wagner; Pol Maria Rommens Journal: PLoS One Date: 2020-05-21 Impact factor: 3.240
Authors: Francesco Maria Achille Consoli; Yara Bernaldo de Quirós; Manuel Arbelo; Stefania Fulle; Marco Marchisio; Mario Encinoso; Antonio Fernandez; Miguel A Rivero Journal: Animals (Basel) Date: 2022-07-13 Impact factor: 3.231