A back propagation neural network based respiratory motion modelling method

IF 2.3 3区医学 Q2 SURGERY International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2024-05-28 DOI:10.1002/rcs.2647

Shan Jiang, Bowen Li, Zhiyong Yang, Yuhua Li, Zeyang Zhou

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Abstract

Background

This study presents the development of a backpropagation neural network-based respiratory motion modelling method (BP-RMM) for precisely tracking arbitrary points within lung tissue throughout free respiration, encompassing deep inspiration and expiration phases.

Methods

Internal and external respiratory data from four-dimensional computed tomography (4DCT) are processed using various artificial intelligence algorithms. Data augmentation through polynomial interpolation is employed to enhance dataset robustness. A BP neural network is then constructed to comprehensively track lung tissue movement.

Results

The BP-RMM demonstrates promising accuracy. In cases from the public 4DCT dataset, the average target registration error (TRE) between authentic deep respiration phases and those forecasted by BP-RMM for 75 marked points is 1.819 mm. Notably, TRE for normal respiration phases is significantly lower, with a minimum error of 0.511 mm.

Conclusions

The proposed method is validated for its high accuracy and robustness, establishing it as a promising tool for surgical navigation within the lung.

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基于反向传播神经网络的呼吸运动建模方法。

背景：本研究提出了一种基于反向传播神经网络的呼吸运动建模方法（BP-RMM），用于在整个自由呼吸过程中精确跟踪肺组织内的任意点，包括深吸气和呼气阶段：方法：使用各种人工智能算法处理来自四维计算机断层扫描（4DCT）的内部和外部呼吸数据。通过多项式插值进行数据增强，以提高数据集的鲁棒性。然后构建一个 BP 神经网络，以全面跟踪肺组织运动：结果：BP-RMM 显示出了良好的准确性。在公共 4DCT 数据集的案例中，75 个标记点的真实深度呼吸相位与 BP-RMM 预测相位之间的平均目标配准误差（TRE）为 1.819 毫米。值得注意的是，正常呼吸相位的目标注册误差要小得多，最小误差为 0.511 毫米：所提出的方法因其高精度和鲁棒性而得到验证，使其成为肺部手术导航的理想工具。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.