Robot-Assisted 3D ICE Catheter for Cardiac Ablation

用于心脏消融的机器人辅助 3D ICE 导管

• 批准号: 9973517
• 负责人: Jayender Jagadeesan
• 金额: $ 43.73万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-15 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9973517
• 关键词: 3-Dimensional; Address; Adopted; Adoption; Algorithms; Anatomy; Area; Arrhythmia; Atrial Fibrillation; Canada; Cardiac; Cardiac ablation; Catheters; Computer Vision Systems; Computer software; Coupling; Diagnosis; Early Diagnosis; Echocardiography; Ensure; Esophageal Fistula; Family suidae; Fluoroscopy; Goals; Grant; Heart; Heart Atrium; Hospitals; Image; Intuition; Ionizing radiation; Lead; Lesion; Machine Learning; Manuals; Maps; Medical; Microbubbles; Modeling; Monitor; Myocardium; Navigation System; Necrotic Lesion; Ontario; Operative Surgical Procedures; Patient Care; Patients; Perforation; Performance; Positioning Attribute; Procedures; Pulmonary veins; Radial; Recurrence; Research; Research Personnel; Risk; Robot; Robotics; Roentgen Rays; Surgical Instruments; System; Technology; Thrombus; Time; Ultrasonography; United States; Universities; Vision; Woman; Work; base; cancer risk; clinical care; convolutional neural network; deep learning algorithm; design; dexterity; image guided; innovation; instrument; kinematics; light weight; machine learning algorithm; machine vision; novel; radio frequency; real time monitoring; robot assistance; therapy outcome; time use

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.

抽象的 尽管心房颤动的心脏消融手术的采用率很高，但也具有很高的 心律不齐的复发主要是由于次优病变的产生。该过程也关联 并发症，包括心脏穿孔，填充剂，心脏食道瘘和血栓。重复 临床医生长时间的X射线暴露也会导致癌症的风险增加。一种荧光素的方法 由于这种情况，使用心内超声心动图（ICE）已成为一种更广泛的成像选项 没有电离辐射以及对创建病变实时监测的可能性。但是，冰 - 引导方法遭受了重大缺点，包括冰导管的敏捷性差，难度 在同时操纵冰和消融导管时，不直觉的图像方向和嘈杂的图像 质量。因此，没有必要克服冰才能实现的这些缺点以实现 与心脏消融过程相关的更好的病变创造和减少并发症。长期 这项研究的目的是开发机器人技术，控制和机器学习算法，以使冰融化 引导心脏消融程序。目的是开发一个新型的机器人操纵器，可辨认的冰 导管以及机器学习和控制算法来操纵冰导管并监视创建的 实时病变。拟议研究的基础是机器人辅助的可通行冰 带有导管跟踪算法的导管将同时操纵冰和消融 导管。此外，用于监测治疗的机器学习算法将降低并发症的风险，而 确保形成坏死病变，从而减少AF的复发。在此提案中，我们计划 追求以下特定目的：1）设计，开发和建模可通道的3D冰导管，并增强 灵巧。 2）设计和开发机器人操纵器和关联的控制算法，以精确 操纵冰导管。 3）开发与A的基于机器学习和基于视觉的算法 用于跟踪消融导管和监测疗法的导航系统。 4）验证机器人冰系统 在心脏幻影和猪模型中。拟议的研究很重要，因为它将允许更好的治疗 通过降低与心脏消融相关的复发率和并发症的结果，并避免 患者和临床护理团队暴露于X射线辐射。拟议的研究具有创新性 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 引导心脏消融程序。