E-Poster Presentation ESA-SRB-ANZBMS 2021

Three-dimensional quantitative morphometric analysis (QMA) for longitudinally tracking tibiofemoral microstructure in a preclinical mouse model (#711)

Han Liu 1 , Jemima E. Schadow 1 , Pholpat Durongbhan 1 , Mateus O. Silva 1 , Kathryn S. Stok 1
  1. Department of Biomedical Engineering, The University of Melbourne, Parkville, Australia

Osteoarthritis (OA) is a degenerative joint disease, causing structural changes in the whole joint. Recent studies showed three-dimensional quantitative morphometric analysis (QMA) is a valuable tool to track joint change from micro-computed tomography (microCT) images. However, it has only been applied in ex vivo cross-sectional studies to date [1-2]. In this study, we tested the feasibility of an in vivo longitudinal protocol for evaluating joint and bone QMA in a preclinical mouse model, with a goal for future use in OA studies. The knees of four healthy C57Bl/10 mice were scanned weekly for 8 weeks using microCT (vivaCT80, Scanco Medical) at a nominal voxel size of 10 µm. 3D QMA was performed on the dataset to evaluate changes to epiphyseal trabecular and cortical bone, and the lateral and medial joint. These joint parameters include center of mass vector length λ, and orientation α, β, γ, lateral and medial joint space width JSWL, JSWM, and lateral and medial joint space volume JSVL, JSVM [1]. All joint parameters showed little deviation over time (Fig. 1). Specifically, λ was consistent in length and orientation, where most deviation is observed in γ, which is associated with joint flexion. Likewise, little change to epiphyseal trabecular bone (BV/TV, Tb.Th, Tb.N and Tb.S) and cortical bone (Ct.Th, Ct.Po) was observed. This study demonstrates feasibility of an in vivo longitudinal protocol for evaluating joint and bone QMA in a preclinical mouse model. As expected in a skeletally mature animal, joint parameters are stable over time. Small deviations are expected and highlight the need for consistent positioning of the joint. Next steps will focus on applying the protocol to an OA dataset to reveal changes associated with disease progression, as well as histopathological analysis for validation.611f0c7c39612-Figure+1_final.png

  1. [1] Stok, KS, et al., PLoS ONE, 2016, vol. 11, no. 1, pp. 1–23.
  2. [2] Besler, B. A. et al., Bone, 2021, 146.