Development of an automatic real-time personalized non-invasive localization of the site of origin of the earliest ventricular activation

项目摘要 心脏骤停是发达国家死亡的主要原因之一， 在美国每年大约有35万人死亡。这些事件中的大多数是由 室性心律失常（VA）。植入式心脏起搏器可降低高风险患者的死亡率，但不能 预防复发性心律失常。可以抑制复发性室性心动过速（VT） 有效地与导管消融;最近立体定向体放射治疗（SBRT）已被证明 有潜在的作用。准确鉴定负责VA的底物是成功的关键。 这些模态中的任何一种，并且可以使用标准12导联ECG来非侵入性地识别 局灶性室性心动过速（VT）/室性早搏（PVC）发生的部位或 其中折返回路离开中央峡部以激活“正常”心肌。目前还 不是一种自动实时非侵入性患者特异性方法，可用于准确识别 使用12导联ECG测量VA的起源部位（SoO）。 快速12导联心电图判读以确定VA的SoO需要专业知识，并且可以得到促进 使用计算机化方法实时自动定位VT出口/PVC起源部位。的能力 准确识别室性心动过速出口/PVC起源部位，使电生理学家能够集中精力 映射/定位到特定区域。为了解决这个问题，本研究提出开发一种新的 非侵入性3D标测技术，依赖于个性化心室表面的组装， CT/MRI扫描结合来自大型临床数据库的统计估计， 根据真实的诱发/记录的VT/PVC ECG识别VT出口/PVC起源部位。该项目 跨学科，因为它结合了生物医学工程，临床心脏电生理学，ECG 信号处理、图像处理和计算统计建模。为此，该项目将 包括以下两项活动：1）在临床可用软件中开发拟议系统; 2） 在前瞻性病例系列研究中评估拟议软件的准确性（目标是实现 平均定位误差小于10 mm）。 该项目交付的拟议软件将显着提高VT的准确性 出口/PVC起源部位定位，可能缩短侵入性VA消融术的时间，以及 将有助于无创心脏SBRT的准确靶向VT。拟议的项目具有创新性 在提出将整合结构数据（CT/MRI成像）的计算统计建模时， 功能数据（ECG）和大型临床数据集进入当代患者护理领域。在其核心， 该项目具有平移性质，个性化的计算统计建模用于 指导临床治疗。

Project Summary Sudden cardiac arrest is one of the leading causes of death in developed countries, accounting for approximately 350,000 deaths per year in the United States. The majority of those events are caused by ventricular arrhythmias (VA). Implantable defibrillators reduce mortality in high-risk patients, but do not prevent recurrent arrhythmias. Suppression of recurrent ventricular tachycardia (VT) can be accomplished effectively with catheter ablation; more recently stereotactic body radiotherapy (SBRT) has been shown to have a potential role. Accurate identification of the substrate responsible for the VA is key to the success of either of these modalities and may be facilitated using the standard 12-lead ECG to noninvasively identify the site from which a focal ventricular tachycardia (VT)/premature ventricular complex (PVC) arises or from which a reentrant circuit exits the central isthmus to activate the “normal” myocardium. Currently, there is not an automatic real-time non-invasive patient-specific approach that can be used to accurately identify the site of origin (SoO) of VA using the 12-lead ECG. Rapid 12-lead ECG interpretation to identify the SoO of VA requires expertise and could be facilitated with a computerized method to automatically locate the VT exit/PVC origin site in real-time. The ability to accurately identify the VT exit/PVC origin site enables the electrophysiologist to concentrate mapping/targeting to a specific region. To tackle this problem, this research proposes to develop a novel non-invasive 3D mapping technique that relies on the assembly of personalized ventricular surfaces from CT/MRI scans in combination with a statistical estimate derived from a large clinical database to accurately identify the VT exit/PVC origin site from an induced/recorded VT/PVC ECG in real time. The project is interdisciplinary as it combines expertise in biomedical engineering, clinical cardiac electrophysiology, ECG signal processing, image processing, and computational statistical modeling. To this end, the project will include the following two activities: 1) to develop the proposed system in clinically usable software; 2) to assess the accuracy of the proposed software in a prospective case-series study (with the goal of achieving a mean localization error of less than 10 mm). The proposed software delivered by this project will provide significant accuracy improvement in the VT exit/PVC origin site localization, potentially decrease in the time of an invasive VA ablation procedure, and would be helpful to accurately target VT for non-invasive cardiac SBRT. The proposed project is innovative in proposing to bring computational statistical modeling that integrates structure data (CT/MRI imaging), function data (ECG), and a large clinical dataset into the realm of contemporary patient care. At its core, the project is of translational nature, with personalized computational statistical modeling being used for guidance of clinical therapies.