Insights into Imaging (Apr 2021)

Three-dimensional preoperative planning in the weight-bearing state: validation and clinical evaluation

  • Tabitha Roth,
  • Fabio Carrillo,
  • Matthias Wieczorek,
  • Giulia Ceschi,
  • Hooman Esfandiari,
  • Reto Sutter,
  • Lazaros Vlachopoulos,
  • Wolfgang Wein,
  • Sandro F. Fucentese,
  • Philipp Fürnstahl

DOI
https://doi.org/10.1186/s13244-021-00994-8
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 11

Abstract

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Abstract 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.

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