arXiv 2510.170382025-10-19cs.ROcs.AIcs.CV

DINO-CVA：一种用于自主导管导航的多模态目标条件视觉到动作模型

DINO-CVA: A Multimodal Goal-Conditioned Vision-to-Action Model for Autonomous Catheter Navigation

Gina Cody School of Engineering and Computer Science, Concordia University（甘娜·柯迪工程与计算机科学学院，康科迪亚大学）；The Wilfred and Joyce Posluns Centre for Image Guided Innovation & Therapeutic Intervention (PCIGITI) at the Hospital for Sick Children (SickKids)（威廉与乔伊斯·波斯卢斯影像引导创新与治疗干预中心（PCIGITI）（SickKids医院））；Electrical and Computer Engineering Department, Kettering University（电气与计算机工程系，凯特林大学）

Pedram Fekri, Majid Roshanfar, Samuel Barbeau, Seyedfarzad Famouri, Thomas Looi, Dale Podolsky, Mehrdad Zadeh, Javad Dargahi

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AI总结：

本文提出多模态目标条件行为克隆框架 DINO-CVA，融合视觉与操纵杆运动学并自回归预测导管动作，在合成血管模型实验中达到高精度，验证自主导管导航可行性。

摘要：

心导管插入术仍是微创介入治疗的基石，但它继续严重依赖手动操作。尽管机器人平台取得了进展，现有系统本质上仍主要是跟随领导者式的，需要医生持续输入，缺乏智能自主性。这种依赖会导致操作者疲劳、更多辐射暴露以及手术结果的差异。本工作通过提出 DINO-CVA 这一多模态目标条件行为克隆框架，迈向自主导管导航。所提出的模型将视觉观测和操纵杆运动学融合到联合嵌入空间中，使策略同时具备视觉感知和运动学感知能力。模型根据专家演示自回归地预测动作，并通过目标条件引导导航到达指定目的地。研究设计了一个配备合成血管模型的机器人实验装置，用于采集多模态数据集并评估性能。结果表明，DINO-CVA 在动作预测方面达到高精度，性能与仅使用运动学的基线相当，同时还使预测锚定于解剖环境。这些发现证实了多模态、目标条件架构用于导管导航的可行性，代表着在减少操作者依赖并提高基于导管治疗可靠性方面迈出的重要一步。

英文摘要：

Cardiac catheterization remains a cornerstone of minimally invasive interventions, yet it continues to rely heavily on manual operation. Despite advances in robotic platforms, existing systems are predominantly follow-leader in nature, requiring continuous physician input and lacking intelligent autonomy. This dependency contributes to operator fatigue, more radiation exposure, and variability in procedural outcomes. This work moves towards autonomous catheter navigation by introducing DINO-CVA, a multimodal goal-conditioned behavior cloning framework. The proposed model fuses visual observations and joystick kinematics into a joint embedding space, enabling policies that are both vision-aware and kinematic-aware. Actions are predicted autoregressively from expert demonstrations, with goal conditioning guiding navigation toward specified destinations. A robotic experimental setup with a synthetic vascular phantom was designed to collect multimodal datasets and evaluate performance. Results show that DINO-CVA achieves high accuracy in predicting actions, matching the performance of a kinematics-only baseline while additionally grounding predictions in the anatomical environment. These findings establish the feasibility of multimodal, goal-conditioned architectures for catheter navigation, representing an important step toward reducing operator dependency and improving the reliability of catheterbased therapies.

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