A novel method for estimating three-dimensional patellofemoral kinematics using optical motion capture with a knee marker grid, and a shape fitting algorithm

Abstract

Background: Abnormal patellofemoral joint loading patterns may cause patellofemoral pain and may be caused by patellar maltracking. Treatment is aimed at restoring normative patellofemoral kinematics. For this, an accurate estimation of the patellofemoral kinematics is necessary. While 4-dimensional computed tomography (4D-CT) can provide this by capturing bone geometries, this equipment is not commonly available in clinical settings and requires radiation. Optical Motion Capture (OMC) with a grid of markers overlying the bone has shown promising results for measuring scapular and patellofemoral kinematics.
Objective: The aim of this study is to develop and validate a new method to estimate patellofemoral kinematics from OMC by applying a shape fitting algorithm to a knee marker grid.
Methods: Five participants were equipped with a knee marker grid and performed a prone extension-flexion movement during which kinematics were measured using OMC and 4D-CT. Patellofemoral kinematics were estimated using three methods: Geometry-based 4D-CT, Grid-based 4D-CT, and Grid-based OMC. In Geometry-based 4D-CT, PF kinematics were obtained from bone geometries. In both Grid-based methods, a shape fitting algorithm using an Iterative Closest Point algorithm estimated the patellofemoral kinematics from the locations of the grid markers. For validation, both 4D-CT methods were compared, as well as both Grid-based methods. To quantify the validity of the new method, root mean square errors (RMSEs) of the differences between the methods and Spearman correlation coefficients were computed.
Results: For Geometry-based 4D-CT vs. Grid-based 4D-CT, the RMSEs of the differences were 7° for flexion and 3 to 10 mm for all translations, combined with very high correlations. The RMSEs for tilt and spin were over 18°, combined with low and moderate negative correlations. For Grid-based 4D-CT vs. Grid-based OMC, RMSEs were smallest for ML translation (4 mm) and spin and tilt (< 6°), but these were paired with moderate correlations. On the other hand, the higher RMSEs for flexion (9°) and AP and SI translation (> 10 mm) were paired with moderate to very high correlations.
Conclusion: Comparing the 4D-CT methods indicated that spin and tilt could not be measured accurately using the marker grid, which are both clinical relevant. Comparing the Grid-based methods showed large differences between OMC and 4D-CT, probably introduced by the accuracy of the measurement system. Overall, the shape fitting algorithm thus seems to be able to estimate PF flexion and all three PF translations from a marker grid, but applying it to OMC data is not valid yet.