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Updated: Jan 13, 2026
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
Published on: November 23, 2019
Statistical shape model-based estimation of registration error in computer-assisted total knee arthroplasty
Behnaz Gheflati1, Morteza Mirzaei2, Joel Zuhars2
1Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada. b_ghefla@encs.concordia.ca.
Purpose:
Computer-assisted surgical navigation systems have been developed to improve the precision of total knee arthroplasty (TKA) by providing real-time guidance on implant alignment relative to patient anatomy. However, surface registration remains a key source of error that can propagate through the surgical workflow. This study investigates how patient-specific femoral bone geometry influences registration accuracy, aiming to enhance the reliability and consistency of computer-assisted orthopedic procedures.
Methods:
Eighteen high-fidelity 3D-printed femur models were used to simulate intraoperative digitization. Surface points collected from the distal femur were registered to preoperative CT-derived models using a rigid iterative closest point (ICP) algorithm. Registration accuracy was quantified across six degrees of freedom. An in-house statistical shape model (SSM), built from 114 CT femurs, was employed to extract shape coefficients and correlate them with the measured registration errors. To verify robustness, additional analyses were conducted using synthetic and in silico CT-based femur datasets.
Results:
Significant correlations (p-values < 0.05) were observed between specific shape coefficients and registration errors. The third and fourth principal shape modes showed the strongest associations with rotational misalignments, particularly flexion-extension and varus-valgus components. These findings demonstrate that geometric variability in the distal femur, especially condylar morphology, plays a major role in determining the stability and accuracy of surface-based registration.
Conclusions:
Registration errors in TKA are strongly influenced by patient-specific bone geometry. Shape features derived from statistical shape models can serve as reliable predictors of registration performance, providing quantitative insight into how anatomical variability impacts surgical precision and alignment accuracy in computer-assisted total knee arthroplasty.
