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• W2894090008 abstract "ObjectiveOur aim is to establish methods for quantifying morphometric properties of calcified cartilage (CC) from micro-computed tomography (μCT). Furthermore, we evaluated the feasibility of these methods in investigating relationships between osteoarthritis (OA), tidemark surface morphology and open subchondral channels (OSCCs).MethodSamples (n = 15) used in this study were harvested from human lateral tibial plateau (n = 8). Conventional roughness and parameters assessing local 3-dimensional (3D) surface variations were used to quantify the surface morphology of the CC. Subchondral channel properties (percentage, density, size) were also calculated. As a reference, histological sections were evaluated using Histopathological osteoarthritis grading (OARSI) and thickness of CC and subchondral bone (SCB) was quantified.ResultsOARSI grade correlated with a decrease in local 3D variations of the tidemark surface (amount of different surface patterns (rs = −0.600, P = 0.018), entropy of patterns (EP) (rs = −0.648, P = 0.018), homogeneity index (HI) (rs = 0.555, P = 0.032)) and tidemark roughness (TMR) (rs = −0.579, P = 0.024). Amount of different patterns (ADP) and EP associated with channel area fraction (CAF) (rp = 0.876, P < 0.0001; rp = 0.665, P = 0.007, respectively) and channel density (CD) (rp = 0.680, P = 0.011; rp = 0.582, P = 0.023, respectively). TMR was associated with CAF (rp = 0.926, P < 0.0001) and average channel size (rp = 0.574, P = 0.025). CC topography differed statistically significantly in early OA vs healthy samples.ConclusionWe introduced a μ-CT image method to quantify 3D CC topography and perforations through CC. CC topography was associated with OARSI grade and OSCC properties; this suggests that the established methods can detect topographical changes in tidemark and CC perforations associated with OA." @default.
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• W2894090008 date "2019-01-01" @default.
• W2894090008 modified "2023-10-09" @default.
• W2894090008 title "3D morphometric analysis of calcified cartilage properties using micro-computed tomography" @default.
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• W2894090008 doi "https://doi.org/10.1016/j.joca.2018.09.009" @default.
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