Despite the success of total knee arthroplasty, there continues to be a significant proportion of patients who are dissatisfied. One explanation may be a shape mismatch between pre- and postoperative distal femurs. The purpose of this study was to investigate methods suitable for matching a statistical shape model (SSM) to intraoperatively acquired point cloud data from a surgical navigation system and to validate these against the preoperative magnetic resonance imaging (MRI) data from the same patients.
A total of 10 patients who underwent navigated total knee arthroplasty also had an MRI scan <2 months preoperatively. The standard surgical protocol was followed which included partial digitization of the distal femur. Two different methods were employed to fit the SSM to the digitized point cloud data, based on (1) iterative closest points and (2) Gaussian mixture models. The available MRI data were manually segmented and the reconstructed three-dimensional surfaces used as ground truth against which the SSM fit was compared.
For both approaches, the difference between the SSM-generated femur and the surface generated from MRI segmentation averaged less than 1.7 mm, with maximum errors occurring in less clinically important areas.
The results demonstrated good correspondence with the distal femoral morphology even in cases of sparse datasets. Application of this technique will allow for measurement of mismatch between pre- and postoperative femurs retrospectively on any case done using the surgical navigation system and could be integrated into the surgical navigation unit to provide real-time feedback.