Robot-Assisted 3D ICE Catheter for Cardiac Ablation

摘要 尽管房颤的心脏消融手术被广泛采用，但它的使用率也很高。 心律失常的复发主要是由于产生了不理想的病变。该过程也与 并发症包括心脏穿孔、填塞、食道房瘘和血栓。重复 对于临床医生来说，长时间接触X射线也会增加患癌症的风险。一种无氟方法 使用心内超声心动图(ICE)正成为一种更广泛采用的成像选择，因为 没有电离辐射，并有可能实时监测所产生的损伤。然而，洲际交易所- 导引入路有明显的缺点，包括ICE导管的灵活性差，困难 在同时操作ICE和消融导管时，图像方向不直观，图像有噪声 质量。因此，有一种尚未得到满足的需要，即克服由独立专家委员会指导的方法的这些缺点，以使 更好地创建病变并减少与心脏消融程序相关的并发症。长期的 这项研究的目标是开发机器人技术、控制和机器学习算法，以使ICE- 引导的心脏消融程序。目标是开发一种新型的机器人操作手，一种可操纵的内燃机 导管，以及机器学习和控制算法，以操作ICE导管并监控创建的 实时的损伤。这项研究的基本原理是，机器人辅助的可控内燃机 具有导管跟踪算法的导管将能够同时操作ICE和消融 导尿管。此外，监测治疗的机器学习算法将降低并发症的风险，而 确保坏死性病变的产生，从而减少房颤的复发。在这项提案中，我们计划 追求以下具体目标：1)设计、开发和建模可定向的3D ICE导管 灵巧。2)设计和开发机器人机械手和相关控制算法，以实现精确 ICE导管的操作。3)开发机器学习和基于视觉的算法，与 用于跟踪消融导管和监测治疗的导航系统。4)验证机器人内燃机系统 心中的幽灵和猪的模型。这项拟议的研究意义重大，因为它将允许更好的治疗 通过减少复发率和心脏消融相关的并发症和避免 患者和临床护理团队暴露在X射线辐射下。拟议的研究具有创新性，因为它 建立在最先进的机器人技术、机器学习和视觉算法的基础上，使无氟冰- 引导的心脏消融程序。

ABSTRACT Although the cardiac ablation procedure for atrial fibrillation has a wide adoption rate, it also has a high recurrence of arrhythmia primarily due to the creation of suboptimal lesions. The procedure is also associated with complications including cardiac perforation, tamponade, atrio-esophageal fistulas and thrombus. Repeated and prolonged X-ray exposure for the clinician can also lead to enhanced risk of cancer. A fluoroless approach using intracardiac echocardiography (ICE) is becoming a more widely adopted imaging option due to the absence of ionizing radiation and the possibility of real-time monitoring of the created lesions. However, the ICE- guided approach suffers from significant shortcomings, which include poor dexterity of the ICE catheter, difficulty in simultaneously manipulating the ICE and ablation catheters, unintuitive image orientation and noisy image quality. There is therefore an unmet need to overcome these shortcomings of the ICE-guided approach to enable better lesion creation and reduced complications associated with the cardiac ablation procedure. The long-term goal of this research is to develop robotic technologies, control and machine learning algorithms to enable ICE- guided cardiac ablation procedures. The objective is to develop a novel robotic manipulator, a steerable ICE catheter, and machine learning and control algorithms to manipulate the ICE catheter and monitor the created lesions in real-time. The rationale that underlies the proposed research is that the robot-assisted steerable ICE catheter with the catheter tracking algorithms will enable simultaneous manipulation of the ICE and ablation catheters. Further, the machine learning algorithms to monitor therapy will reduce the risk of complications, while ensuring the creation of necrotic lesions, thereby reducing the recurrence of AF. In this proposal, we plan to pursue the following specific aims: 1) Design, develop, and model a steerable 3D ICE catheter with enhanced dexterity. 2) Design and develop a robotic manipulator and associated control algorithms to allow for precise manipulation of the ICE catheter. 3) Develop machine-learning and vision-based algorithms integrated with a navigation system for tracking the ablation catheter, and monitoring therapy. 4) Validate the robotic ICE system in heart phantom and porcine models. The proposed research is significant since it will allow for better therapeutic outcomes by reducing recurrence rates and complications associated with cardiac ablation, and avoiding exposure of the patient and clinical care team to X-ray radiation. The proposed research is innovative in that it builds on state-of-the-art robotics technology, machine learning and vision algorithms to enable fluoroless ICE- guided cardiac ablation procedures.