Micro-CT analysis of trabecular parameters gradients in femurs of mice affected by chronic kidney disease

12 March 2018 Micro-CT analysis of trabecular parameters gradients in femurs of mice affected by chronic kidney disease

Daniel W. Shin, Alexander R. Podgorsak, Kenneth Seldeen, Lee Chaves, Shruthi Thomas, Amanda Honan, Sham Abyad, Bruce Troen, Rabi Yacoub, Ciprian N. Ionita

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Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 105781M (2018) https://doi.org/10.1117/12.2293567
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Chronic kidney disease (CKD) is associated with gradual bone loss that occurs from the failure of the kidneys to regulate bone mineralization. Degradation of bone structure can be quantified with the usage of Micro-CT. The current methods of quantitative imaging typically use a single region of interest (ROI) that segments the whole trabecular region and obtain bone parameters, which usually are not homogenous across such a large ROI. Here we introduce a novel method of quantifying bone parameters that can be used to determine overall bone health. This method analyzes sequential regions on the trabecular bone with multiple small ROIs and evaluates the gradients of bone parameters across these ROIs. Two C57Bl/6J mice femur groups were prepared: a control and CKD groups. All femurs were scanned with a Micro-CT system using tube voltage of 60 kV and current of 0.667 mA. Femur volumes were reconstructed with the Feldkamp-Davis-Kress algorithm and were imported into MicroView to perform bone analysis. Six different sequential ROIs were selected at different distances from the growth plate (0.5mm increments). The gradients of bone parameters along the ROI distance for the control and CKD group were compared. Significant differences were found between two groups in the gradients of bone volume density (P = 0.0002), connective density (P = 0.0003), trabecular spacing (P = 0.001), and trabecular number (P = 0.01). As a result, our method identified a sharp change in several parameters representing a novel and biologically significant strategy.

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Daniel W. Shin, Alexander R. Podgorsak, Kenneth Seldeen, Lee Chaves, Shruthi Thomas, Amanda Honan, Sham Abyad, Bruce Troen, Rabi Yacoub, and Ciprian N. Ionita "Micro-CT analysis of trabecular parameters gradients in femurs of mice affected by chronic kidney disease", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105781M (12 March 2018); https://doi.org/10.1117/12.2293567

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KEYWORDS

Bone

Gaussian filters

Image filtering

Kidney

Image segmentation

Animal model studies

Biological research

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