融合形态特征的基于GRU的介入机器人导丝轨迹预测建模

张任飞; 董林杰; 王兴松; 田梦倩; 苏浩波

doi:10.16383/j.aas.c250506

融合形态特征的基于GRU的介入机器人导丝轨迹预测建模

doi: 10.16383/j.aas.c250506 cstr: 32138.14.j.aas.c250506

1.
东南大学机械工程学院南京 211189
2.
南京市第一医院血管介入科南京 210006

基金项目: 国家自然科学基金(52175005)资助

详细信息

作者简介:
张任飞：东南大学机械工程学院博士研究生. 主要研究方向为介入手术机器人的导航与控制. E-mail: 230218562@seu.edu.cn

董林杰：东南大学机械工程学院博士研究生. 主要研究方向为基于深度学习的图像处理与人工智能. E-mail: 230228047@seu.seu.cn

王兴松：博士, 东南大学机械工程学院教授. 主要研究方向为机器人动力学及其控制, 先进医疗器械等. 本文通信作者. E-mail: xswang@seu.edu.cn

田梦倩：博士, 东南大学机械工程学院副教授. 主要研究方向为智能机器人及控制技术, 机电控制及自动化. E-mail: Tianmq@seu.edu.cn

苏浩波：博士, 南京市第一医院介入血管科副主任医师. 主要研究方向为脑血管疾病及良恶性肿瘤的介入诊疗. E-mail: doctorsuhaobo@163.com

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出版历程
- 收稿日期: 2025-09-29
- 录用日期: 2025-12-19
- 网络出版日期: 2026-02-12

GRU-Based Modeling for Predicting Guidewire Trajectories in Interventional Robotics with Morphological Feature Fusion

1.
School of Mechanical Engineering, Southeast University, Nanjing 211189
2.
Department of Interventional and Vascular Radiology, Nanjing First Hospital, Nanjing 210006

Funds: Supported by National Natural Science Foundation of China (52175005)

More Information

Author Bio:
ZHANG Ren-Fei　Ph.D. candidate at the School of Mechanical Engineering, Southeast University. His research interest is navigation and control of interventional surgical robots

DONG Lin-Jie　Ph.D. candidate at the School of Mechanical Engineering, Southeast University. His research interest include deep-learning-based image processing and artificial intelligence

WANG Xing-Song　Ph.D., professor at the School of Mechanical Engineering, Southeast University. His research interest include robot dynamics and control, and advanced medical devices. Corresponding author of this paper

TIAN Meng-Qian　Ph.D., associate professor at the School of Mechanical Engineering, Southeast University. Her research interest include intelligent robots and control technology, and mechatronic control and automation

SU Hao-Bo　Ph.D., associate chief physician at the Department of Interventional Vascular Surgery, Nanjing First Hospital. His research interest include interventional diagnosis and treatment of cerebrovascular diseases and benign and malignant tumors

摘要

摘要: 在介入手术场景中, 导丝整体轨迹的预测对导航安全至关重要. 提出一种基于门控循环单元(GRU)的因果时序建模框架: 将导丝物理属性, 血管接触区形态特征与操作环境参数中的序列级常量(刚度, 进入角度, 摩擦系数等)按时间步广播, 与动态几何量(中心线坐标, 直径等)拼接, 经两层特征编码后输入单向GRU, 逐时回归二维坐标. 针对变长序列, 提出时间步长度分类策略训练机制, 在不改网络结构的前提下提升收敛与适配能力. 实验结果表明, 在多类导丝与多进入角度条件下, 模型在保持因果性的同时兼具准确性与实时性: 最小误差0.40 mm, 平均误差0.46 mm, 最大误差0.54 mm; 相较未采用分类策略的基线, 收敛epoch降低42%, 训练用时降低52%, 单次推理时延降低51%. 本研究为介入机器人导丝轨迹建模与术中导航提供了可部署的算法基础.
- 介入机器人 /
- 导丝轨迹预测 /
- 门控循环单元(GRU) /
- 形态特征融合 /
- 时序建模
Abstract: We present a causality-preserving sequential estimator for guidewire trajectory reconstruction during interventional navigation. Unlike generic recurrent baselines, the proposed model time-broadcasts sequence-level constants---including stiffness, insertion angle, and an effective friction descriptor---and concatenates them with dynamic geometric tokens (centerline coordinates and local diameter) before a two-stage feature encoder and a unidirectional GRU decoder that emits 2-D positions stepwise. To cope with variable sequence lengths, we adopt a length-bucketed curriculum with mask-based loss, which limits padding-induced gradients and improves efficiency without altering the network architecture. On a phantom platform covering multiple guidewire types and insertion angles, the method achieves a 0.40--0.54~mm position-error range (mean 0.46~mm) while preserving strict causality; relative to a non-bucketed baseline, it reduces epochs-to-convergence by 42%, training time by 52%, and per-inference latency by 51%. These results indicate a deployable, real-time basis for guidewire trajectory estimation and intraoperative navigation.
- Interventional robotics /
- guidewire trajectory prediction /
- gated Recurrent Unit (GRU) /
- morphological feature fusion /
- sequential modeling

HTML全文

图 1 血管内介入与细长机器人辅助引导系统

Fig. 1 Endovascular intervention and slender robot assisted guidance system

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图 2 通过导丝预测模型预测导丝轨迹示意图

Fig. 2 Schematic illustration of the guidewire shape prediction pipeline

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图 3 基于GRU的导丝整体轨迹预测模型

Fig. 3 Workflow of the GRU-based trajectories prediction model

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图 4 血管结构的形态特征测量结果示意图

Fig. 4 Illustration of morphological feature measurements results for vascular structures

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图 5 用户实验平台的组成结构示意图

Fig. 5 Schematic diagram of the user experiment platform and its components

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图 6 多目标任务中导丝轨迹真实与预测对比图

Fig. 6 Comparison of predicted and actual guidewire trajectories in multi-target tasks

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图 7 导丝插入任务中实拍图像上模型预测轨迹可视化

Fig. 7 Visualization of model-predicted trajectories overlaid on real captured images in guidewire insertion tasks

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图 8 用户实验中不同导丝与插入角度组合下的MAE和CV

Fig. 8 MAE and CV across different guidewire and insertion angle configurations

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表 1 时间步分类策略对模型实时性的影响

Table 1 Impact of the time-step classification strategy on model real-time performance

是否使用时间步分类策略	epoch数量	训练用时	单次推理时延
是	377	0.221h	11.571ms
否	653	0.457h	23.783ms

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