用模仿学习让软体导丝自动导航，精准到达动脉瘤位置。
Toward Autonomous Soft Robotic Endovascular Navigation via Imitation Learning
- 用Transformer框架+目标条件控制，实现软体导丝自主导航。
- 在3个未见过的血管结构上成功率达83%，优于多个基线方法。
- 可推广至真实患者血管模型，适合手术机器人研发人员参考。
在血管内介入手术中，医生通过导管和导丝在血管内引导至病灶部位治疗血栓、动脉瘤等。传统机械导丝操控困难，难以建模。本研究提出一种基于模仿学习的软体导丝自主导航方法，构建了2D投影的大规模仿真环境，采用带目标条件的Transformer框架，支持相对动作输出与自动对比剂注射，用于动脉瘤靶向任务。在36种模块化分叉血管几何结构上生成647组模拟透视下的示范数据，训练策略后在3个未见的血管几何上测试，成功率达83%。消融实验验证各设计有效性。进一步扩展至一个来自真实患者的血管结构，仍达到75%成功率。项目主页：https://softrobotnavigation.github.io/
原文摘要 · Abstract (English)
In endovascular surgery, endovascular interventionists push a thin tube called a catheter, guided by a thin wire to a treatment site inside the patient's blood vessels to treat various conditions such as blood clots, aneurysms, and malformations. Robotic guidewires can enhance maneuverability but are difficult to model and control. Autonomous soft robotic guidewire navigation has the potential to overcome these challenges, increasing the precision and safety of endovascular navigation. As a first step, we establish a large-scale, 2D-projected environment for autonomous navigation. In other surgical domains, end-to-end imitation learning has shown promising results. Thus, we develop a transformer-based imitation learning framework with goal conditioning, relative action outputs, and automatic contrast dye injections to enable generalizable soft robot navigation in an aneurysm targeting task. We train the policy on 36 different modular bifurcated geometries, generating 647 total demonstrations under simulated fluoroscopy, and evaluate it on three previously unseen vascular geometries. The policy reaches the aneurysm with a success rate of 83% on the unseen geometries, outperforming several baselines. In addition, ablation and baseline studies evaluate the effectiveness of each design and data collection choice. Lastly, we extend the policy to achieve 75% success on an unseen patient-derived geometry. Project website: https://softrobotnavigation.github.io/
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