Quantitative morphometric analysis (QMA) using micro-computed tomography (microCT) allows non-invasive assessment of structural degradation of joint tissues, such as bone and cartilage, during osteoarthritis (OA) progression in small animal knees. Whole-joint features, such as joint space width and joint alignment, were shown to provide sensitive markers of biomechanical changes due to OA. As elastic links, the relative position of joint components can vary. Combined with traditional manual image processing and analysis approaches that requires recalibration between studies, QMA of the joint often encounters reproducibility issues that are technically challenging and time-consuming to tackle.

The general aim of this thesis is to examine how improvements in image processing protocols can lead to reproducible and dynamic quantitative analysis of joint morphology. In this work, reproducibility issues in the acquisition, processing, and analysis routines were tackled by addressing the following hypotheses:​

1. Introduction of contrast agents and control of knee pose with a positioning device during microCT acquisition allows reproducible and accurate in situ QMA of cartilage and joint.
2. Automating the image processing pipeline through morphological representation using spherical harmonics improves the efficiency and reproducibility of joint QMA.
3. An empirical probabilistic approach provides an automatic and model-invariant solution to statistically capture morphological changes due to OA by detecting osteophyte activity.

In response to the first hypothesis, a whole animal positioning device and a cationic contrast agent injection protocol were developed for in situ microCT morphological assessments of the mouse knee. By securing the mouse using the device for consistent landmark location and limb stabilisation, high reproducibility of joint QMA results was achieved. Combined with the optimised contrast agent injection protocol, accurate measurements of cartilage attenuation and QMA were concurrently observed.

Addressing the second hypothesis, an automatic workflow based on spherical harmonics and persistent homology was developed to process the acquired microCT images for joint QMA. Spherical harmonics, describing the basic shape of the tibia, were used to align the joint to a common reference coordinate system. Anatomically meaningful subdivision of the joint into lateral and medial volume of interests was subsequently performed using a watershed method based on persistent homology. Validated on microCT scans of rabbit and rat knees, joint QMA results from images processed through the automated workflow show excellent reproducibility with significantly reduced time and personnel training requirements compared to manual processing.

Tackling the final hypothesis, an empirical probabilistic approach was developed for an automatic and model-invariant QMA of osteophyte activity. Osteophyte disrupts the integrity of the cortical exterior, leading to a rougher surface with smaller local thickness values. The method statistically captures these changes by identifying shifts in local thickness as an indicator of osteophyte activity. Validated on microCT datasets of rabbit and rat knees, the method and the resulting models were shown to be objective and invariant to animal scale. Using this approach, reproducibility can be maintained across studies by removing the need for parametric recalibrations to adjust for animal scale and voxel size.

As these approaches are invariant to animal scale and voxel size, the need for recalibration in subsequent studies has been removed. By improving the acquisition, processing, and analysis protocols, this work increases the reproducibility and efficiency of QMA workflows, leading to higher throughput and more dynamic QMA outcomes