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Global and site-specific analysis of bone in a rat model of spinal cord injury-induced osteoporosis. | LitMetric

AI Article Synopsis

Article Abstract

Micro-Computed Tomography bone analysis is the gold standard method for assessing trabecular and cortical bone microarchitecture in small animal bones. This technique reports morphometric parameters as averages over selected volumes of interest (VOIs). This study proposes the introduction of an additional global 2D morphometric step into the analysis process, that provides a survey of the underlying morphometric variation present throughout both trabecular and cortical bone. The visualisation of these morphometric distributions provides a systematic approach to VOI selection that provides rationale and adds confidence to subsequent 3D morphometric analysis. To test the applicability and value of this methodological addition it was applied to the distal femur of a rat model of spinal cord injury (SCI)-induced osteoporosis. The 2D morphometric variation of both trabecular and cortical bone was quantified as a function of bone length. SCI-induced osteoporosis was localised in i) trabecular bone, where metaphyseal bone was more severely affected than epiphyseal bone, and there was a significant reduction in Distal Femoral Trabecular Extent, a new parameter defined here that quantifies how far trabecular bone penetrates in to the marrow cavity, ii) cortical bone, where diaphyseal bone underwent significant lowering of both cortical area and thickness, while distal-metaphyseal bone did not. Theses site-specific changes were validated, further elucidated and compared with follow-up conventional 3D analysis. The techniques applied here are equally applicable to other long bones (tibia, humerus, radius, ulna), other types of imaging modality and other types of experimental design including the effects of rehabilitation, aging, loading, gene knockout and pharmacological intervention.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6920718PMC
http://dx.doi.org/10.1016/j.bonr.2019.100233DOI Listing

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