Improved Targeting and Assessment of Electrophysiology Intervention

描述（由申请人提供）：房颤（AF）和室性心动过速（VT）影响美国数百万患者。这些心律失常可以用导管消融治愈，但心律失常经常复发，这些复发通常是由于不完全消融造成的可逆传导阻滞。无法确认所需位置是否存在完全消融的病变是消融后VT复发率超过40%和AF复发率超过30%的主要因素。此外，目前的技术不可能充分预测室性心动过速通过疤痕的途径，这是消融的目标。该计划的总体目标是使用先进的基于图像的技术来改善电生理干预的靶向和评估。在最初的5年资助期内，目标是开发所需的技术，定义临床系统，并证明该方法的可行性。这些目标已经实现。在此更新资助期内，目标是在室性心动过速消融的临床研究中使用已开发的技术，继续改进技术，并将范围扩大到房颤消融的临床研究。我们假设高分辨率磁共振成像（MRI）与兼容电极导管、定位传感器、标测系统、实时扫描仪控制和计算建模可以（1）帮助预测心律失常回路的位置（2）帮助预测关键消融靶点的位置，（3）提供准确的导管导航到这些关键靶点，（4）真实的时间监测消融损伤的形成，以及（5）评估消融的完整性。一旦得到验证，这些增强的功能可以显著改善复杂消融手术的结果，成为未来的消融方法，并成为改善许多其他干预措施结果的平台。在目前的项目中，我们开发了重要的创新方法和MRI兼容版本的消融设备，用于预测室性心动过速消融靶点，在MRI扫描仪中进行消融，以及病变成像。我们开发了区分不完全消融（可逆性损伤）组织和完全消融（坏死）组织的成像方法。这允许确定是否存在完全的损伤坏死，或者在手术期间是否需要额外的消融来完成消融，从而减少复发。在这项新提案中，我们将把这些创新技术应用于临床消融研究，因为它们已经代表了对当前方法的实质性改进。我们将继续开发改进的技术，以充分实现MRI引导消融的潜力。我们将把我们以前对室性心动过速消融的关注扩展到房颤消融，并强调手术有效性的研究。该项目是约翰霍普金斯大学医学、放射学和生物医学工程系;希巴医疗中心;以及行业合作伙伴：圣犹达（基于阻抗的跟踪系统）、Greatbatch（导管组件）和西门子（术中计算建模）之间的合作。

DESCRIPTION (provided by applicant): Atrial fibrillation (AF) and ventricular tachycardia (VT) affect millions of patients in the United States. These arrhythmias can be cured with catheter ablation, but the arrhythmias often recur, and these recurrences are generally due to reversible conduction block from incomplete ablation. The inability to confirm the presence of completely ablated lesions in the desired locations is the major factor in the greater than 40% recurrence of VT after ablation, and the greater than 30 % recurrence of AF after ablation. In addition, it is no possible with current technology to adequately predict the pathways of VT through scar, which are the targets for ablation. The overall goal of the program is to use advanced, image-based technologies to improve targeting and assessment of electrophysiology intervention. In the initial 5-year funding period, the goals were to develop needed technology, define the clinical system, and demonstrate the feasibility of the approach. Those goals were achieved. In this renewal funding period, the goals are to use the developed technology in clinical studies of VT ablation, continue to improve the technology, and expand the scope into clinical studies of AF ablation. We hypothesize that high-resolution Magnetic Resonance Imaging (MRI) with compatible electrode catheters, location sensors, mapping systems, real-time scanner control, and computational modeling, can (1) aid in predicting the locations of arrhythmia circuits (2) aid in predicting the locations of critical ablation targets, (3) provide for accurate catheter navigationto those critical targets, (4) monitor the formation of ablation lesions in real time, and (5) assess the completeness of ablation. Once validated, these enhanced capabilities could dramatically improve the outcomes from complex ablation procedures, become the ablation methodology of the future, and become a platform for improving outcomes from many other interventions. In the current program, we developed important, innovative methods, and MRI-compatible versions of ablation equipment, for predicting VT ablation targets, for performing ablations in an MRI scanner, and for lesion imaging. We developed imaging methods that differentiate incompletely ablated (reversibly damaged) tissue from completely ablated (necrotic) tissue. This allows determination of whether there is complete lesion necrosis, or whether additional ablation is needed during the procedure to complete the ablation, and, thereby, reduce recurrences. In this new proposal, we will apply these innovative technologies to clinical ablation studies, since they already represent a substantial improvement over current methods. We will continue development of improved technologies for full realization of the potential for MRI guided ablation. We will expand our previous focus on ablation of VT to ablation of AF, and emphasize studies of procedure efficacy. This project is a partnership between the Johns Hopkins University Departments of Medicine, Radiology, and Biomedical Engineering; Sheba Medical Center; and industrial partners: St Jude (impedance-based tracking system), Greatbatch (catheter components), and Siemens (intra-procedural computational modeling).