Quantifying subresolution 3D morphology of bone with clinical computed tomography
Karhula, S. S.; Finnilä, M. A. J.; Rytky, S. J. O.; Cooper, D. M.; Thevenot, J.; Valkealahti, M.; Pritzker, K. P. H.; Haapea, M.; Joukainen, A.; Lehenkari, P.; Kröger, H.; Korhonen, R. K.; Nieminen, H. J.; Saarakkala, S. (2019-10-03)
Karhula, S.S., Finnilä, M.A.J., Rytky, S.J.O. et al. Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography. Ann Biomed Eng 48, 595–605 (2020). https://doi.org/10.1007/s10439-019-02374-2
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https://urn.fi/URN:NBN:fi-fe2020041718949
Tiivistelmä
Abstract
The aim of this study was to quantify sub-resolution trabecular bone morphometrics, which are also related to osteoarthritis (OA), from clinical resolution cone beam computed tomography (CBCT). Samples (n = 53) were harvested from human tibiae (N = 4) and femora (N = 7). Grey-level co-occurrence matrix (GLCM) texture and histogram-based parameters were calculated from CBCT imaged trabecular bone data, and compared with the morphometric parameters quantified from micro-computed tomography. As a reference for OA severity, histological sections were subjected to OARSI histopathological grading. GLCM and histogram parameters were correlated to bone morphometrics and OARSI individually. Furthermore, a statistical model of combined GLCM/histogram parameters was generated to estimate the bone morphometrics. Several individual histogram and GLCM parameters had strong associations with various bone morphometrics (|r| > 0.7). The most prominent correlation was observed between the histogram mean and bone volume fraction (r = 0.907). The statistical model combining GLCM and histogram-parameters resulted in even better association with bone volume fraction determined from CBCT data (adjusted R2 change = 0.047). Histopathology showed mainly moderate associations with bone morphometrics (|r| > 0.4). In conclusion, we demonstrated that GLCM- and histogram-based parameters from CBCT imaged trabecular bone (ex vivo) are associated with sub-resolution morphometrics. Our results suggest that sub-resolution morphometrics can be estimated from clinical CBCT images, associations becoming even stronger when combining histogram and GLCM-based parameters.
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