Learning Physics and Physiology Based Cardiac Activation Imaging

该项目的目标：了解预测体表电信号的替代模型使用患者特定的解剖和体表电信号校正替代模型了解测量心脏整体或区域激活的导联位置使用SCAR图像改善激活成像验证激活成像每次跳动时，心脏从顶部两个心腔(心房)开始激活，然后激活较低的两个心腔(心室)。这种协调的激活调节心脏对血液的有效泵送。激活模式的崩溃可导致严重的心功能障碍，了解激活的时间和位置在诊断和计划治疗中至关重要。测量心脏激活的标准方法是心电图，但对ECG的解释可能具有挑战性，而且ECG不提供激活模式的详细图像。可以侵入性地测量地面真相激活模式，但这是以卫生系统的成本和患者的风险为代价的。我们可以成像心脏的运动、形状和组织，但这些并不能为推断激活模式提供可靠的信息。目前推断脑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，这个身体表面可以用来估计心脏表面的电势。这个问题是不适定的，因此使用正则化方法来约束该问题。电极背心价格昂贵，限制了激活成像的广泛采用。目前的心电成像(ECGi)方法忽略了基本的生理学，不能估计不确定性，并且不能与其他成像数据相结合。另一种方法是根据所有可用的数据创建特定于患者的基于物理的模型，并根据患者的成像和电记录对该模型进行校准。伦敦国王学院是这种建模方法的领先中心；然而，这些模型庞大且昂贵，使得传统的模拟方法与激活成像不兼容。在这个项目中，我们建议利用机器学习建模的发展来学习高成本和保真度的心脏电激活和心电图计算模型的最佳低成本近似。通过针对详细的物理和基于生理的模型来训练模型代理人，我们将能够推断激活并为所推断的激活模式提供机制解释。该项目将首先使用模拟数据开发一个电子成像工作流程的原型。然后将该方法应用于回溯性临床数据。最后，我们将前瞻性地比较接受临床指征手术的患者的推断和侵入性测量的激活模式。该项目将结合图像和信号分析、建模和模拟、机器学习和人工智能技术来创建和测试一种新的心脏激活成像系统。

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.