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用临床计算机断层扫描定量骨的亚分辨率 3D 形态。

Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography.

机构信息

Research Unit of Medical Imaging, Physics and Technology, University of Oulu, POB 5000, 90014, Oulu, Finland.

Infotech, University of Oulu, Oulu, Finland.

出版信息

Ann Biomed Eng. 2020 Feb;48(2):595-605. doi: 10.1007/s10439-019-02374-2. Epub 2019 Oct 3.

DOI:10.1007/s10439-019-02374-2
PMID:31583552
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6949315/
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 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.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4f4/6949315/04b10adea742/10439_2019_2374_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4f4/6949315/2bffb32decf7/10439_2019_2374_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4f4/6949315/e634ebf98528/10439_2019_2374_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4f4/6949315/04b10adea742/10439_2019_2374_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4f4/6949315/2bffb32decf7/10439_2019_2374_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4f4/6949315/e634ebf98528/10439_2019_2374_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f4f4/6949315/04b10adea742/10439_2019_2374_Fig3_HTML.jpg

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