Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography

Abstract

The aim of this study was to quantify subresolution 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 2 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.

Discrimination of Low-Energy Acetabular Fractures from Controls Using Computed Tomography-Based Bone Characteristics

“…18,41 Information measure of correlation (IMC) is a measure of texture complexity derived from mutual information as defined by Haralick et al 18 Lastly, the variables in each of the 13 directions were averaged to ensure rotational invariance. 26,39 Bone Density Assessment Using Gray Value Histogram Analyses…”

“…6,39 Previous CT based studies using relatively larger slice thickness (£ 5 mm) 6,7,29 and pixel spacing (£ 1 mm) 38,44 have demonstrated that it is possible to extract useful information under low-resolution settings. Hence, trabecular architectural features defined by trabecular structure, texture and density can be extracted from clinical CT. 1,26,39 Bone quantity can be characterized by bone volume fraction (BV/TV). 39 Trabecular texture can be analyzed using various methods such as fractal dimensions 24 and gray level co-occurrence matrix (GLCM).…”

“…39 Trabecular texture can be analyzed using various methods such as fractal dimensions 24 and gray level co-occurrence matrix (GLCM). 26,39 In addition, bone mineral density can be also estimated from the first-order statistics of the gray value (GV) histogram within a 3D volume of interest (VOI). 22,26 Moreover, bone microstructure measured from clinical CT has been shown to be associated with BV/TV, 1 histogram-based GV density 26,42 and textural features.…”

“…26,39 In addition, bone mineral density can be also estimated from the first-order statistics of the gray value (GV) histogram within a 3D volume of interest (VOI). 22,26 Moreover, bone microstructure measured from clinical CT has been shown to be associated with BV/TV, 1 histogram-based GV density 26,42 and textural features. 26,39 Machine learning, unlike traditional statistics, is a useful approach when trying to assess a predictive outcome from a large number of input variables.…”

“…22,26 Moreover, bone microstructure measured from clinical CT has been shown to be associated with BV/TV, 1 histogram-based GV density 26,42 and textural features. 26,39 Machine learning, unlike traditional statistics, is a useful approach when trying to assess a predictive outcome from a large number of input variables. 40 Several studies have previously been conducted that employ traditional statistical approaches to discriminate subjects with and without femoral neck fractures by measuring high-resolution trabecular architectural variables.…”