In this section
@article{Abel:2013:10.13172/2050-2303-2-5-952,
author = {Abel, RL and Prime, M and Jin, A and Cobb, JP and Bhattacharya, R},
doi = {10.13172/2050-2303-2-5-952},
journal = {Hard Tissue},
title = {3D Imaging Bone Quality: Bench to Bedside},
url = {http://dx.doi.org/10.13172/2050-2303-2-5-952},
volume = {2},
year = {2013}
}
TY - JOUR
AB - IntroductionMeasuring the health of bone is important for understanding the pathogenesis, progression, diagnosis and treatment outcomes for fragility. At present the most common method for measuring bone health in a clinical setting is to assess skeletal mass. The current gold standard is dual-energy X-ray absorptiometry (DXA) which models bones as 2D objects and measures areal bone mineral density (BMD). However, BMD only accounts for 50% of bone strength and the technique ignores other important factors such as cortical geometry and trabecular architecture, which are also significant contributors. Consequently a new concept of ‘bone quality’ has developed the material and structural basis of bone strength and fragility. As yet though, a suitable non-invasive method has not been developed for measuring quality in living patients. The aim of this paper is to discuss how bone quality might be visualised, quantified and applied in a clinical setting.DiscussionThe most useful imaging techniques are likely to be clinical-CT and MRI. Both modalities have been used successfully to characterise bone macro-structure in 3D e.g. volume fraction and orientation. More recently in vivo systems with high resolution (~0.100–0.200 mm) have been developed that can capture some aspects of bone micro-architecture. Alternatively 3D models created using clinical-CT and MRI can be used to virtually simulate loading on a computer and calculate bone mechanical properties. Analysed together these morphological and mechanical data sets might allow clinicians to provide screening programmes for osteoporosis and calculate individual fracture risk. Especially if applied as part of a holistic approach utilising patient meta-data on risk factors for metabolic bone disease (e.g. FRAX). As well as improve primary and secondary care by setting treat to target criteria for pharmacological therapies and planning surgical interventions or following up treatment outcomes.In the short t
AU - Abel,RL
AU - Prime,M
AU - Jin,A
AU - Cobb,JP
AU - Bhattacharya,R
DO - 10.13172/2050-2303-2-5-952
PY - 2013///
SN - 2050-2303
TI - 3D Imaging Bone Quality: Bench to Bedside
T2 - Hard Tissue
UR - http://dx.doi.org/10.13172/2050-2303-2-5-952
UR - http://hdl.handle.net/10044/1/30697
VL - 2
ER -