Accuracy of Functional and Predictive Methods to Calculate the Hip Joint Center in Young Non-pathologic Asymptomatic Adults with Dual Fluoroscopy as a Reference Standard

Predictions from biomechanical models of gait may be sensitive to joint center locations. Most often, the hip joint center (HJC) is derived from locations of reflective markers adhered to the skin. Here, predictive techniques use regression equations of pelvic anatomy to estimate the HJC, whereas functional methods track motion of markers placed at the pelvis and femur during a coordinated motion. Skin motion artifact may introduce errors in the estimate of HJC for both techniques. Quantifying the accuracy of these methods is an area of open investigation. In this study, we used dual fluoroscopy (DF) (a dynamic X-ray imaging technique) and three-dimensional reconstructions from computed tomography images, to measure HJC locations in vivo. Using dual fluoroscopy as the reference standard, we then assessed the accuracy of three predictive and two functional methods. Eleven non-pathologic subjects were imaged with DF and reflective skin marker motion capture. Additionally, DF-based solutions generated virtual markers placed on bony landmarks, which were input to the predictive and functional methods to determine if estimates of the HJC improved. Using skin markers, functional methods had better mean agreement with the HJC measured by DF (11.0 ± 3.3 mm) than predictive methods (18.1 ± 9.5 mm); estimates from functional and predictive methods improved when using the DF-based solutions (1.3 ± 0.9 and 17.5 ± 8.6 mm, respectively). The Harrington method was the best predictive technique using both skin markers (13.2 ± 6.5 mm) and DF-based solutions (10.6 ± 2.5 mm). The two functional methods had similar accuracy using skin makers (11.1 ± 3.6 and 10.8 ± 3.2 mm) and DF-based solutions (1.2 ± 0.8 and 1.4 ± 1.0 mm). Overall, functional methods were superior to predictive methods for HJC estimation. However, the improvements observed when using the DF-based solutions suggest that skin motion artifact is a large source of error for the functional methods.