Region partitioning of articular cartilage with streaming-potential-based parameters and indentation maps.

Articular cartilage exhibits site-specific tissue inhomogeneity, for which the tissue properties may continuously vary across the articular surface. To facilitate practical applications such as studying site-specific cartilage degeneration, the inhomogeneity may be approximated with several distinct region-wise variations, with one set of tissue properties for one region. A clustering method was previously developed to partition such regions using cartilage indentation-relaxation and thickness mapping instead of simply using surface geometry. In the present study, a quantitative parameter based on streaming potential measurement was introduced as an additional feature to assess the applicability of the methodology with independent datasets. Experimental data were collected from 24 sets of femoral condyles, extracted from fresh porcine stifle joints, through streaming potential mapping, automated indentation, and needle penetration tests. K-means clustering and Elbow method were used to find optimal region partitions. Consistent with previous findings, three regions were suggested for either lateral or medial condyle regardless of left or right joint. The region shapes were approximately triangular or trapezoidal, which was similar to what was found previously. Streaming potentials were confirmed to be region-dependent, but not significantly different among joints. The cartilage was significantly thicker in the medial than lateral condyles. The region areas were consistent among joints, and comparable to that found in a previous study. The present study demonstrated the capability of region partitioning methods with different variables, which may facilitate new applications whenever site-specific tissue properties must be considered.

Characterizing site-specific mechanical properties of knee cartilage with indentation-relaxation maps and machine learning.

Knee cartilage experiences site-specific focal lesion and degeneration, which is likely associated with tissue inhomogeneity and nonuniform mechanical stimuli in the joint, for which a complete picture remains to be depicted. The present study aimed to develop a methodology to quantify knee cartilage inhomogeneity using porcine knee specimens. Automated indentation-relaxation and needle probing were performed on fully intact cartilage to obtain data that essentially represent continuous distributions of cartilage properties in the knee. Machine learning was then introduced to approximate the tissue inhomogeneity with several regions via clusters of indentation locations, and finite element modeling was used to obtain poromechanical properties for each region using indentation-relaxation and thickness data. Significant region dependence was established from the full time-dependent mechanical response. Seventeen regions, or clusters, were found to best approximate the site-specific poromechanical properties of articular cartilage for femoral groove, lateral and medial condyles and tibial plateaus, after up to eight clusters were tested for each of the five cartilage sections. The region partitions recommended, and tissue properties acquired would facilitate implementation of tissue inhomogeneity in future applications, e.g., contact modeling of the knee joint. The results obtained from 14 porcine knees revealed interesting region differences, for example, the two condyles have the same effective stiffness when responding to slowly applied mechanical loadings but substantially lower stiffness in the medial condyle when responding to fast loadings. This mechanical behavior may be associated with the fact that medial femoral cartilage is more prone to focal lesions than the lateral one.