A MODEL-INVARIANT APPROACH FOR 3D ASSESSMENT OF STRUCTURAL CHANGES IN PRECLINICAL ANIMAL MODELS OF OA

Abstract

Quantitative morphometric analysis (QMA) of joint tissues has been shown to be sensitive to OA in preclinical rabbit and rat models [1,2]. Traditionally performed using workflows based on morphological operations such as dilation and erosion, its sensitivity is dependent on animal scale and voxel size, requiring parametric studies to be performed in every new implementation. We hypothesize that using probabilistic approaches, it is possible to capture OA structural changes while remaining invariant to the animal model and voxel size. In this work, a pilot study developing an empirical and model-invariant approach is conducted to automatically assess osteophyte presence, a hallmark feature of OA. Develop an empirical probabilistic approach providing an automatic and model-invariant solution to statistically capture morphological changes due to OA by detecting osteophyte activity. Micro-computed tomography scans (SCANCO Medical AG, Switzerland) from previous rabbit and rat studies of OA were used [1,2]. The rabbit dataset (18 μm voxel size) consists of scans from rabbits that underwent ACL transection in the right knee (rabbit OP, n = 8), while the left knee was kept as contralateral control (rabbit NO, n = 8). The rat dataset (10 μm voxel size) consists of scans from rats that underwent ACL transection and medial meniscectomy in the right knee (rat OP, n = 11), while the other was kept as contralateral control (rat NO, n = 11). For each image of the joint, the cortical bone of the femur and tibia were segmented, and its 3D thickness was calculated using a sphere-fitting distance transform [3]. A one-voxel-thick outer layer was segmented from the resulting thickness map, and its thickness histogram was empirically estimated by a series of statistical distributions. Parameters describing the best-fit distribution, determined with negative log-likelihood, were analyzed to determine their sensitivity to OA and the presence of osteophytes using paired two-sided t-tests. Visual inspections revealed clustering of surface voxels with small thickness values around areas with osteophytes (Figure 1a), as compared to those without (Figure 1b). Surface thickness values were optimally fitted to a Gamma distribution using negative log-likelihood for most femur and tibia across both animals. The shape parameter of the fitted Gamma distribution was found to be vary significantly with OA (p < 0.01) and in the presence of osteophytes (p < 0.05). By combining traditional image processing with empirical distribution fitting, an automated and model-invariant method for structural assessment that is sensitive to OA and the presence of osteophyte can be achieved. Future work will focus on characterizing the underlying biological processes of abnormal bone remodeling to gain insight into the emergence of the fitted Gamma distribution observed in this study. Discovery Projects scheme from the Australian Research Council (DP180101838). None CORRESPONDENCE ADDRESS: [email protected]