Image-Guided Intervention Group, Research Centre of Biomedical Technology and Robotics RCBTR, Tehran University of Medical Sciences, Tehran, Iran.
Int J Comput Assist Radiol Surg. 2014 Jan;9(1):39-48. doi: 10.1007/s11548-013-0907-6. Epub 2013 Jun 20.
In recent years, image-guided liver surgery based on intraoperative ultrasound (US) imaging has become common. Using an efficient point-based registration method to improve both accuracy and computational time for the registration of predeformation computer tomography, liver images with postdeformation US images are important during surgical procedure. Although iterative closest point (ICP) algorithm is widely used in surface-based registration, its performance strongly depends on the presence of noise and initial alignment. A registration technique based on unscented Kalman filter (UKF), which has been proposed recently, can used to overcome the noise and outliers on an incremental basis; however, the technique is associated with computational complexity.
To overcome the limitations of ICP and UKF algorithms, we proposed an incremental two-stage registration method based on the combination of ICP and UKF algorithms to update the registration process with the acquired new points from US images. The registration is based on both the vessels and surface information of the liver.
The two-stage method was examined using numerical simulations and phantom data sets. The results of the phantom data set confirmed that the two-stage method outperforms the accuracy of ICP by 23% and reduces the running time of UKF by 60%.
The convergence rate, computational speed, and accuracy of the UKF algorithm can be improved using the two-stage method.
近年来，基于术中超声（US）成像的图像引导肝切除术已变得普遍。使用高效的基于点的配准方法来提高预变形计算机断层扫描图像与变形后 US 图像的配准精度和计算时间，这在手术过程中非常重要。虽然迭代最近点（ICP）算法广泛应用于基于表面的配准，但它的性能强烈依赖于噪声和初始配准的存在。最近提出的基于无迹卡尔曼滤波器（UKF）的配准技术可以用于逐步克服噪声和离群值；然而，该技术与计算复杂度相关。
为了克服 ICP 和 UKF 算法的局限性，我们提出了一种基于 ICP 和 UKF 算法相结合的增量两阶段配准方法，该方法可以使用从 US 图像中获取的新点来更新配准过程。配准基于肝脏的血管和表面信息。
使用数值模拟和体模数据集对两阶段方法进行了检验。体模数据集的结果证实，两阶段方法比 ICP 提高了 23%的精度，并将 UKF 的运行时间减少了 60%。
使用两阶段方法可以提高 UKF 算法的收敛速度、计算速度和准确性。
