Roth Tabitha, Carrillo Fabio, Wieczorek Matthias, Ceschi Giulia, Esfandiari Hooman, Sutter Reto, Vlachopoulos Lazaros, Wein Wolfgang, Fucentese Sandro F, Fürnstahl Philipp
Institute for Biomechanics, ETH Zurich, Leopold-Ruzicka-Weg 4, 8093, Zurich, Switzerland.
Research in Orthopedic Computer Science (ROCS), University Hospital Balgrist, University of Zurich, Balgrist Campus, Lengghalde 5, 8008, Zurich, Switzerland.
Insights Imaging. 2021 Apr 7;12(1):44. doi: 10.1186/s13244-021-00994-8.
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.
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.
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.
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.
鉴于现代手术技术,下肢截骨术的三维术前规划变得越来越重要。然而,三维模型通常是根据在非负重姿势下获取的计算机断层扫描数据重建的,因此忽略了负重所引入的位置变化。我们开发了一种配准和规划流程,可实现负重条件下解剖畸形的三维术前规划及后续三维评估。
采用基于强度的算法将CT扫描与长腿站立位X线片进行配准,随后将患者特异性三维模型转换为负重状态。开发了机械轴以及关节线汇聚角的三维测量方法。使用腿部模型对该流程进行了验证。此外,我们通过将其应用于59例患者的放射学数据对我们的方法进行了临床评估。
在三维空间中评估了配准精度,最大平移误差和旋转误差分别为1.1毫米(内外侧方向)和1.2°(上下轴)。临床评估证明了该方法在真实患者数据上的可行性,并且在考虑负重影响时三维测量结果存在显著差异。机械轴和关节线汇聚角的平均差异分别为2.1±1.7°和2.0±1.6°。37.3%和40.7%的患者在负重和非负重状态下机械轴或关节线汇聚角的差异达2°或更大。
我们提出的方法提供了一种临床可行的方法,可在术前融合二维负重和三维非负重数据,以优化手术矫正。