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一种用于鼻窦和颅底内镜手术导航的增量配准方法:从体模研究到临床试验。

An incremental registration method for endoscopic sinus and skull base surgery navigation: From phantom study to clinical trials.

机构信息

Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China.

Ariemedi Medical Technology (Beijing) Co., Ltd., Beijing, China.

出版信息

Med Phys. 2023 Jan;50(1):226-239. doi: 10.1002/mp.15941. Epub 2022 Aug 31.

Abstract

PURPOSE

Surface-based image-to-patient registration in current surgical navigation is mainly achieved by a 3D scanner, which has several limitations in clinical practice such as uncontrollable scanning range, complicated operation, and even high failure rate. An accurate, robust, and easy-to-perform image-to-patient registration method is urgently required.

METHODS

An incremental point cloud registration method was proposed for surface-based image-to-patient registration. The point cloud in image space was extracted from the computed tomography (CT) image, and a template matching method was applied to remove the redundant points. The corresponding point cloud in patient space was incrementally collected by an optically tracked pointer, while the nearest point distance (NPD) constraint was applied to ensure the uniformity of the collected points. A coarse-to-fine registration method under the constraints of coverage ratio (CR) and outliers ratio (OR) was then proposed to obtain the optimal rigid transformation from image to patient space. The proposed method was integrated in the recently developed endoscopic navigation system, and phantom study and clinical trials were conducted to evaluate the performance of the proposed method.

RESULTS

The results of the phantom study revealed that the proposed constraints greatly improved the accuracy and robustness of registration. The comparative experimental results revealed that the proposed registration method significantly outperform the scanner-based method, and achieved comparable accuracy to the fiducial-based method. In the clinical trials, the average registration duration was 1.24 ± 0.43 min, the target registration error (TRE) of 294 marker points (59 patients) was 1.25 ± 0.40 mm, and the lower 97.5% confidence limit of the success rate of positioning marker points exceeds the expected value (97.56% vs. 95.00%), revealed that the accuracy of the proposed method significantly met the clinical requirements (TRE ⩽ 2 mm, p < 0.05).

CONCLUSIONS

The proposed method has both the advantages of high accuracy and convenience, which were absent in the scanner-based method and the fiducial-based method. Our findings will help improve the quality of endoscopic sinus and skull base surgery.

摘要

目的

目前手术导航中的基于表面的图像到患者配准主要通过 3D 扫描仪来实现,该方法在临床实践中存在许多局限性,例如不可控的扫描范围、操作复杂,甚至失败率高。因此,迫切需要一种准确、鲁棒且易于执行的图像到患者配准方法。

方法

我们提出了一种基于增量点云的表面图像到患者配准方法。从计算机断层扫描(CT)图像中提取图像空间中的点云,并应用模板匹配方法去除冗余点。通过光学跟踪指针,以递增的方式采集患者空间中的对应点云,同时应用最近点距离(NPD)约束以确保采集点的均匀性。然后,提出了一种基于覆盖比(CR)和异常值比(OR)的粗到精配准方法,以获得从图像到患者空间的最佳刚体变换。该方法已集成到最近开发的内窥镜导航系统中,并进行了体模研究和临床试验以评估该方法的性能。

结果

体模研究的结果表明,所提出的约束条件极大地提高了配准的准确性和鲁棒性。对比实验结果表明,所提出的配准方法显著优于基于扫描仪的方法,并且达到了与基于基准点的方法相当的准确性。在临床试验中,294 个标记点(59 名患者)的平均配准时间为 1.24 ± 0.43 分钟,目标配准误差(TRE)为 1.25 ± 0.40 毫米,标记点定位成功率的 97.5%置信下限(97.56%)显著超过预期值(95.00%),表明该方法的准确性显著满足临床要求(TRE ⩽ 2 毫米,p<0.05)。

结论

所提出的方法具有高精度和方便的优点,弥补了基于扫描仪的方法和基于基准点的方法的不足。我们的研究结果将有助于提高鼻内镜鼻窦和颅底手术的质量。

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