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基于增强粒子群优化的多模态信息融合的稳健电磁引导内镜手术。

Robust electromagnetically guided endoscopic procedure using enhanced particle swarm optimization for multimodal information fusion.

作者信息

Luo Xiongbiao, Wan Ying, He Xiangjian

机构信息

Robarts Research Institute, Western University, London, Ontario N6A 5K8, Canada.

School of Computing and Communications, University of Technology, Sydney, New South Wales 2007, Australia.

出版信息

Med Phys. 2015 Apr;42(4):1808-17. doi: 10.1118/1.4915285.

Abstract

PURPOSE

Electromagnetically guided endoscopic procedure, which aims at accurately and robustly localizing the endoscope, involves multimodal sensory information during interventions. However, it still remains challenging in how to integrate these information for precise and stable endoscopic guidance. To tackle such a challenge, this paper proposes a new framework on the basis of an enhanced particle swarm optimization method to effectively fuse these information for accurate and continuous endoscope localization.

METHODS

The authors use the particle swarm optimization method, which is one of stochastic evolutionary computation algorithms, to effectively fuse the multimodal information including preoperative information (i.e., computed tomography images) as a frame of reference, endoscopic camera videos, and positional sensor measurements (i.e., electromagnetic sensor outputs). Since the evolutionary computation method usually limits its possible premature convergence and evolutionary factors, the authors introduce the current (endoscopic camera and electromagnetic sensor's) observation to boost the particle swarm optimization and also adaptively update evolutionary parameters in accordance with spatial constraints and the current observation, resulting in advantageous performance in the enhanced algorithm.

RESULTS

The experimental results demonstrate that the authors' proposed method provides a more accurate and robust endoscopic guidance framework than state-of-the-art methods. The average guidance accuracy of the authors' framework was about 3.0 mm and 5.6° while the previous methods show at least 3.9 mm and 7.0°. The average position and orientation smoothness of their method was 1.0 mm and 1.6°, which is significantly better than the other methods at least with (2.0 mm and 2.6°). Additionally, the average visual quality of the endoscopic guidance was improved to 0.29.

CONCLUSIONS

A robust electromagnetically guided endoscopy framework was proposed on the basis of an enhanced particle swarm optimization method with using the current observation information and adaptive evolutionary factors. The authors proposed framework greatly reduced the guidance errors from (4.3, 7.8) to (3.0 mm, 5.6°), compared to state-of-the-art methods.

摘要

目的

电磁引导内镜手术旨在精确且稳定地定位内镜,在手术过程中涉及多模态感官信息。然而,如何整合这些信息以实现精确且稳定的内镜引导仍是一项挑战。为应对这一挑战,本文基于一种增强粒子群优化方法提出了一个新框架,以有效地融合这些信息来实现准确且连续的内镜定位。

方法

作者使用粒子群优化方法,这是一种随机进化计算算法,来有效地融合多模态信息,包括作为参考框架的术前信息(即计算机断层扫描图像)、内镜摄像头视频以及位置传感器测量值(即电磁传感器输出)。由于进化计算方法通常会限制其可能的早熟收敛和进化因子,作者引入当前(内镜摄像头和电磁传感器的)观测值来增强粒子群优化,并根据空间约束和当前观测值自适应地更新进化参数,从而在增强算法中实现优越性能。

结果

实验结果表明,作者提出的方法提供了一个比现有方法更准确、更稳健的内镜引导框架。作者框架的平均引导精度约为3.0毫米和5.6°,而之前的方法至少为3.9毫米和7.0°。其方法的平均位置和方向平滑度为1.0毫米和1.6°,明显优于其他方法至少(2.0毫米和2.6°)。此外,内镜引导的平均视觉质量提高到了0.29。

结论

基于一种利用当前观测信息和自适应进化因子的增强粒子群优化方法,提出了一个稳健的电磁引导内镜框架。与现有方法相比,作者提出的框架将引导误差从(4.3, 7.8)大幅降低到了(3.0毫米,5.6°)。

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