Summary

Our statistical shape analysis showed that size is the primary geometrical variation factor in the medial meniscus. Shape variations are primarily focused in the posterior horn, suggesting that these variations could influence cartilage contact pressures.

The various disorders of the patellofemoral joint, from pain syndrome to maltracking and arthritis, form a significant subset of knee disorders (Callaghan and Selfe 2007). Several studies have shown significant geography and gender based variation in incidence rates of these disorders and of osteoarthritis in general (Woolf 2003). A number of previous studies have examined patellar shape in this context, focusing primarily on the use of 2D measurements of bony geometry to classify patellar shapes and identify high-risk groups (Baumgartl 1964; Ficat 1970).

Recent developments in imaging and statistical analysis have enabled a more sophisticated approach, characterised by statistical shape models which account for three dimensional shape differences (Bryan 2008). Incorporating soft tissue data into these analyses, however, has been a challenge due to factors including the necessity of multi-modality images, absence of repeatable landmarks, and complexity of the surfaces involved. We present here a novel method which has potential to significantly improve analysis of soft tissue geometry in joints. It is built using Arthron, a UCD-developed biomechanics analysis software package.

The shape modelling process consists of three phases: pre-processing, consistent surface parameterization, and statistical shape analysis. The pre-processing phase consists of several mesh processing operations that prepare the input surfaces for shape modelling. Consistent surface parameterizations are implemented using the minimum description length (MDL) correspondence method (Davies 2002) [Fig. 1]. The statistical shape analysis phase involves the reporting and visualization of geometric variation at the input surface. An algorithm was developed to measure the cartilage thickness at each node on the patellar surface mesh. The initial step in this process was to calculate surface normal vectors at each point. These vectors were then projected through the cartilage surface model in order to calculate the thickness [Fig. 2]. The Matlab software was used to aggregate all cartilage thickness values in a given subgroup and after being normalised for the average patellar centroid size for the subgroup, these thicknesses were visualised on the average shape.

Pilot study data consisted of 19 Caucasian (7 female, 12 male) and 13 Japanese (7 female, 6 male) subjects. These data originated from studies performed by DePuy Orthopaedics Inc. Initial results show ethnicity effects in cartilage thickness to be more significant than gender effects [Fig. 3]. After correcting for patellar centroid size, male subjects display 9% greater average thickness than female subjects, while Caucasian subjects display 17% greater average thickness than Asian subjects. Areas of statistically significant differences (t < 0.05) were found to coincide with expected areas of patellofemoral contact through the flexion cycle, showing the potential for the thickness differential to impact upon patellar kinematics. Principal component analysis of the thickness distributions gives more detailed information about modes of variation.

With further development, this method has potential to enable sophisticated analysis of localised variation in soft tissue geometry, thereby improving understanding of the impact of joint geometry on disease formation.