Research Unit of Medical Imaging, Physics and Technology, University of Oulu, POB 5000, 90014, Oulu, Finland.
Infotech, University of Oulu, Oulu, Finland.
Ann Biomed Eng. 2020 Feb;48(2):595-605. doi: 10.1007/s10439-019-02374-2. Epub 2019 Oct 3.
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 R 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.
本研究的目的是从临床分辨率的锥形束 CT(CBCT)中定量分析亚分辨率的小梁骨形态计量学,这也与骨关节炎(OA)有关。从人类胫骨(N=4)和股骨(N=7)中采集样本。从 CBCT 成像的小梁骨数据中计算灰度共生矩阵(GLCM)纹理和基于直方图的参数,并与从微计算机断层扫描定量的形态计量学参数进行比较。作为 OA 严重程度的参考,对组织学切片进行 OARSI 组织病理学分级。GLCM 和直方图参数分别与骨形态计量学和 OARSI 相关。此外,还生成了一个基于 GLCM/直方图参数的统计模型来估计骨形态计量学。几个单独的直方图和 GLCM 参数与各种骨形态计量学有很强的相关性(|r|>0.7)。直方图均值与骨体积分数之间的相关性最强(r=0.907)。将 GLCM 和直方图参数结合起来的统计模型与从 CBCT 数据确定的骨体积分数的相关性甚至更好(调整后的 R 变化=0.047)。组织病理学显示与骨形态计量学主要有中度相关性(|r|>0.4)。总之,我们证明了从 CBCT 成像的小梁骨(离体)获得的 GLCM 和基于直方图的参数与亚分辨率形态计量学相关。我们的结果表明,亚分辨率形态计量学可以从临床 CBCT 图像中估计出来,当结合直方图和 GLCM 基于参数的相关性甚至更强。