School of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China.
Int J Comput Assist Radiol Surg. 2020 Jun;15(6):989-999. doi: 10.1007/s11548-020-02163-6. Epub 2020 May 2.
PURPOSE: The surface-based registration approach to laparoscopic augmented reality (AR) has clear advantages. Nonrigid point-set registration paves the way for surface-based registration. Among current non-rigid point set registration methods, the coherent point drift (CPD) algorithm is rarely used because of two challenges: (1) volumetric deformation is difficult to predict, and (2) registration from intraoperative visible tissue surface to whole anatomical preoperative model is a "part-to-whole" registration that CPD cannot be applied directly to. We preliminarily applied CPD on surgical navigation for laparoscopic partial nephrectomy (LPN). However, it introduces normalization errors and lacks navigation robustness. This paper presents important advances for more effectively applying CPD to LPN surgical navigation while attempting to quantitatively evaluate the accuracy of CPD-based surgical navigation. METHODS: First, an optimized volumetric deformation (Op-VD) algorithm is proposed to achieve accurate prediction of volume deformation. Then, a projection-based partial selection method is presented to conveniently and robustly apply the CPD to LPN surgical navigation. Finally, kidneys with different deformations in vitro, phantom and in vivo experiments are performed to evaluate the accuracy and effectiveness of our approach. RESULTS: The average root-mean-square error of volume deformation was refined to 0.84 mm. The mean target registration error (TRE) of the surface and inside markers in the in vitro experiments decreased to 1.51 mm and 1.29 mm, respectively. The robustness and precision of CPD-based navigation were validated in phantom and in vivo experiments, and the mean navigation TRE of the phantom experiments was found to be [Formula: see text] mm. CONCLUSION: Accurate volumetric deformation and robust navigation results can be achieved in AR navigation of LPN by using surface-based registration with CPD. Evaluation results demonstrate the effectiveness of our proposed methods while showing the clinical application potential of CPD. This work has important guiding significance for the application of the CPD in laparoscopic AR.
目的：基于表面的腹腔镜增强现实（AR）配准方法具有明显的优势。非刚性点集配准为基于表面的配准铺平了道路。在当前的非刚性点集配准方法中，由于两个挑战，相干点漂移（CPD）算法很少被使用：（1）体积变形难以预测；（2）从术中可见组织表面到整个解剖术前模型的配准是一种“部分到整体”的配准，CPD 不能直接应用。我们初步将 CPD 应用于腹腔镜部分肾切除术（LPN）的手术导航中。然而，它引入了归一化误差，并且缺乏导航鲁棒性。本文提出了将 CPD 更有效地应用于 LPN 手术导航的重要进展，同时尝试定量评估基于 CPD 的手术导航的准确性。
方法：首先，提出了一种优化的体积变形（Op-VD）算法，以实现对体积变形的精确预测。然后，提出了一种基于投影的部分选择方法，以便方便、稳健地将 CPD 应用于 LPN 手术导航。最后，在体外、仿体和体内实验中进行了具有不同变形的肾脏实验，以评估我们方法的准确性和有效性。
结果：体积变形的平均均方根误差细化到 0.84mm。体外实验中表面和内部标记的平均目标配准误差（TRE）分别降低到 1.51mm 和 1.29mm。在仿体和体内实验中验证了基于 CPD 的导航的鲁棒性和精度，并且发现仿体实验的平均导航 TRE 为[公式：见文本]mm。
结论：通过使用基于表面的 CPD 配准，在 LPN 的 AR 导航中可以实现精确的体积变形和稳健的导航结果。评估结果证明了所提出方法的有效性，同时展示了 CPD 在腹腔镜 AR 中的临床应用潜力。这项工作对 CPD 在腹腔镜 AR 中的应用具有重要的指导意义。
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