Learning Physics and Physiology Based Cardiac Activation Imaging

该项目的目标：学习用于预测体表电描记图的替代模型使用患者特定的解剖结构和体表电描记图校准替代模型学习用于测量整个心脏或心脏区域的激活的电极导线位置使用疤痕图像改善激活成像双激活成像每次心跳时，心脏从顶部两个腔室（心房）开始激活，然后激活下部两个腔室（心室）。这种协调的激活调节心脏的血液泵送的效率和有效性。心功能不全是由心功能不全引起的，而心功能不全的诊断和治疗方案的制定都需要了解心功能不全的发生时间和部位。心电图是测量心功能不全的标准方法，但心电图的解释却很困难，而且心电图不能提供心功能不全的详细情况。地面实况激活模式可以侵入性地测量，但这会给卫生系统带来成本，并给患者带来风险。我们可以对心脏的运动、形状和组织进行成像，但是这些不能提供用于推断激活模式的鲁棒信息。目前用于推断心室激活的技术依赖于放置在躯干上的大量电极来测量体表电位。通过将身体视为导体，该身体表面可以用于估计心脏表面上的电势。这个问题是不好的，所以正则化方法被用来约束问题。电极背心昂贵，限制了激活成像的更广泛采用。当前的ECG成像（ECGi）方法忽略了基本的生理学，没有提供不确定性的估计，并且不能与其他成像数据相结合。另一种方法是从所有可用的数据创建基于患者特定物理的模型，并将该模型校准到患者成像和电记录。国王学院伦敦是一个领先的中心，这种类型的建模方法，然而，这些模型是大型和昂贵的解决，使传统的模拟方法与激活imaging.In这个项目中，我们建议利用机器学习建模的发展，学习最佳的低成本近似的高成本和保真度的计算模型的心脏和心电图的电激活。通过针对详细的物理和生理模型训练模型代理人，我们将能够推断激活并为推断的激活模式提供机械解释。该项目将首先使用模拟数据开发原型电成像工作流程。然后将该方法应用于回顾性临床数据。最后，我们将前瞻性地比较接受临床指征程序的患者的推断和侵入性测量的激活模式。该项目将结合联合收割机图像和信号分析，建模和仿真，以及机器学习和人工智能技术来创建和测试新型心脏激活成像系统。

Aims of the Project: Learn surrogate models for predicting body surface electrogramsCalibrate surrogate models using patient-specific anatomy and body surface electrogramsLearn lead locations for measuring activation in the whole or regions of the heartUse scar images to improve activation imagingValidate activation imagingWith each beat, the heart is activated starting in the top two chambers (atria) before activating the lower two chambers (ventricles). This coordinated activation regulates the efficient and effective pumping of blood by the heart. The breakdown of the activation pattern can cause severe cardiac dysfunction and understanding the timing and location of activation is critical in diagnosis and planning therapies.The standard approach to measuring activation in the heart is an ECG but the interpretation of ECGs can be challenging, and ECG do not provide a detailed picture of activation patterns. Ground truth activation patterns can be measured invasively but this comes at a cost to the health system and risk to the patient. We can image the motion, shape, and tissue of the heart but these do not provide robust information for inferring activation patterns.Current techniques for inferring ventricle activation rely on large numbers of electrodes to be placed on the torso to measure the body surface potential. By treating the body as a conductor this body surface can be used to estimate the potential on the surface of the heart. This problem is poorly posed, and so regularising methods are used to constrain the problem. The electrode vests are expensive limiting the wider adoption of activation imaging. The current ECG imaging (ECGi) approach ignores basic physiology, provides no estimate of uncertainty, and cannot be combined with other imaging data.An alternate approach is to create a patient-specific physics-based model from all available data and calibrate this model to patient imaging and electrical recordings. King's College London is a leading centre for this type of modelling approach; however, these models are large and expensive to solve, making conventional simulation approaches incompatible with activation imaging.In this project, we propose to exploit developments in machine learning modelling to learn optimal low-cost approximations for high-cost and fidelity computational models of electrical activation of the heart and ECG. By training model surrogates against detailed physics and physiological-based models, we will be able to both infer the activation and offer mechanistic explanations for the inferred activation pattern. The project will first develop a prototype electrical imaging workflow using simulated data. Then this approach will be applied to retrospective clinal data. Finally, we will prospectively compare inferred and invasively measured activation patterns in patients receiving a clinically indicated procedure.This project will combine image and signal analysis, modelling and simulation, and machine learning and AI technique to create and test a novel cardiac activation imaging system.