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Updated: Nov 9, 2025

Three-Dimensional Preoperative Virtual Planning in Derotational Proximal Femoral Osteotomy
Published on: February 17, 2023
Three-dimensional preoperative planning in the weight-bearing state: validation and clinical evaluation
Tabitha Roth1,2, Fabio Carrillo3, Matthias Wieczorek4
1Institute for Biomechanics, ETH Zurich, Leopold-Ruzicka-Weg 4, 8093, Zurich, Switzerland. tabitha.roth@balgrist.ch.
Objectives:
3D preoperative planning of lower limb osteotomies has become increasingly important in light of modern surgical technologies. However, 3D models are usually reconstructed from Computed Tomography data acquired in a non-weight-bearing posture and thus neglecting the positional variations introduced by weight-bearing. We developed a registration and planning pipeline that allows for 3D preoperative planning and subsequent 3D assessment of anatomical deformities in weight-bearing conditions.
Methods:
An intensity-based algorithm was used to register CT scans with long-leg standing radiographs and subsequently transform patient-specific 3D models into a weight-bearing state. 3D measurement methods for the mechanical axis as well as the joint line convergence angle were developed. The pipeline was validated using a leg phantom. Furthermore, we evaluated our methods clinically by applying it to the radiological data from 59 patients.
Results:
The registration accuracy was evaluated in 3D and showed a maximum translational and rotational error of 1.1 mm (mediolateral direction) and 1.2° (superior-inferior axis). Clinical evaluation proved feasibility on real patient data and resulted in significant differences for 3D measurements when the effects of weight-bearing were considered. Mean differences were 2.1 ± 1.7° and 2.0 ± 1.6° for the mechanical axis and the joint line convergence angle, respectively. 37.3 and 40.7% of the patients had differences of 2° or more in the mechanical axis or joint line convergence angle between weight-bearing and non-weight-bearing states.
Conclusions:
Our presented approach provides a clinically feasible approach to preoperatively fuse 2D weight-bearing and 3D non-weight-bearing data in order to optimize the surgical correction.

