Evaluation of automated statistical shape model based knee kinematics from biplane fluoroscopy

State-of-the-art fluoroscopic knee kinematic analysis methods require the patient-specific bone shapes segmented from CT or MRI. Substituting the patient-specific bone shapes with personalizable models, such as statistical shape models (SSM), could eliminate the CT/MRI acquisitions, and thereby decrease costs and radiation dose (when eliminating CT). SSM based kinematics, however, have not yet been evaluated on clinically relevant joint motion parameters.Therefore, in this work the applicability of SSMs for computing knee kinematics from biplane fluoroscopic sequences was explored. Kinematic precision with an edge based automated bone tracking method using SSMs was evaluated on 6 cadaveric and 10 in-vivo fluoroscopic sequences. The SSMs of the femur and the tibia–fibula were created using 61 training datasets. Kinematic precision was determined for medial–lateral tibial shift, anterior–posterior tibial drawer, joint distraction–contraction, flexion, tibial rotation and adduction. The relationship between kinematic precision and bone shape accuracy was also investigated.The SSM based kinematics resulted in sub-millimeter (0.48–0.81 mm) and approximately 1° (0.69–0.99°) median precision on the cadaveric knees compared to bone-marker-based kinematics. The precision on the in-vivo datasets was comparable to that of the cadaveric sequences when evaluated with a semi-automatic reference method. These results are promising, though further work is necessary to reach the accuracy of CT-based kinematics. We also demonstrated that a better shape reconstruction accuracy does not automatically imply a better kinematic precision. This result suggests that the ability of accurately fitting the edges in the fluoroscopic sequences has a larger role in determining the kinematic precision than that of the overall 3D shape accuracy.