Automating Real-Time Localization of Target Sites in Catheter Ablation of Ventricular Tachycardia

项目摘要 室性心动过速（VT）是心脏病患者死亡和致残的重要原因。 大多数危及生命的室性心动过速发作是由电“短路”引起的， 心肌瘢痕内的狭窄的残存组织。导管消融通过“阻断”治疗瘢痕相关性室性心动过速 形成回路的残存通道，通常位于回路从疤痕退出的部位。定位VT 然而，退出仍然是一个重大挑战。一种常见的方法，称为起搏映射， 室性心动过速出口作为心室激动的起源并决定QRS形态的原理 12-导联心电图（ECG）。因此，它涉及在心脏的各个部位进行重复的电模拟， 直到定位在所有12个ECG导联上再现VT的QRS的部位。当节奏背后的原则- 测绘是经过时间考验的，目前的做法是“试错”性质，需要快速定性 临床医生对ECG的解释可能是耗时且不准确的。本研究提出 利用现代机器学习技术来改革起搏映射背后原理的使用方式。 目的是了解心室激动起源与心电图形态的关系， 它可以从其ECG数据直接预测VT的退出。为此，该项目将包括以下内容 活动：1）开发基于人群的模型，以提供VT出口的术前初始定位 使用标准的12导联ECG; 2）将基于人群的模型与患者特定的模型相结合， 临床可用软件，用于提供术中实时指导，以定位临床手术的出口部位， VT;以及3）评估拟议软件提高起搏效率和准确性的能力- 在前瞻性临床研究中绘制。该项目将由一个多学科小组执行， 计算和临床科学家与富有成效的合作记录。本项目交付的软件 将为临床医生提供实时帮助，以便在最短时间内缩小室性心动过速退出范围， 定位错误。这将大大减少消融多个VT的工作量， 临床医生可以消融手术中看到的更多甚至所有VT。这可能会缩短消融的持续时间 在改善其结果的同时。软件的开发和部署也增加了最小的 日常工作流程的成本或干扰。由于临床实施的障碍较低，它将具有真实的潜力 挑战和改进导管消融的标准实践。

Project Summary Ventricular tachycardia (VT) is an important cause of mortality and morbidity in patients with heart diseases. The majority of life-threatening VT episodes are caused by an electrical "short circuit” that travels through narrow strands of surviving tissue inside myocardial scar. Catheter ablation treats scar-related VT by “blocking” the surviving channel that forms the circuit, commonly at the site the circuit exits from the scar. To localize a VT exit, however, remains a significant challenge. A common approach, known as pace-mapping, utilizes the principle that the VT exit serves as the origin of ventricular activation and determines the QRS morphology on 12-lead electrocardiograms (ECGs). It thus involves repetitive electrical simulation at various sites of the heart, until locating the site that reproduces the QRS of the VT on all 12 ECG leads. While the principle behind pace- mapping is time tested, the current practice is of a "trial-and-error" nature and requires rapid qualitative interpretation of the ECG by clinicians, which can be time-consuming and inaccurate. This research proposes to leverage modern machine learning techniques to reform the way the principle behind pace-mapping is used. It aims to learn the relationship between the origin of ventricular activation and ECG morphology, and then use it to directly predict the exit of a VT from its ECG data. To this end, this project will include the following activities: 1) to develop a population-based model to provide pre-procedural initial localizations of VT exits using standard 12-lead ECG; 2) to integrate the population-based model with a patient-specific model in clinically-usable software to provide intra-procedural real-time guidance for localizing the exit site of a clinical VT; and 3) to assess the ability of the proposed software to improve the efficiency and accuracy of pace- mapping in a prospective clinical study. This project will be carried out by a multidisciplinary team of computational and clinical scientists with a fruitful record of collaboration. The software delivered by this project will provide real-time assistance to clinicians for narrowing down a VT exit with a minimum amount of time and localization errors. This will substantially reduce the workload for ablating multiple VTs, potentially allowing clinicians to ablate more or even all VTs seen in a procedure. This may reduce the duration of an ablation procedure while improving its outcome. The development and deployment of the software also adds minimal cost or distractions to routine workflow. With a low barrier to clinical implementation, it will have a real potential to challenge and improve the standard practice of catheter ablation.