Intra-procedural updating of cardiac digital-twins for automated arrhythmia ablation target guidance using novel electroanatomical system

使用新型电解剖系统对心脏数字孪生进行程序内更新，以实现自动心律失常消融目标引导

• 批准号: 2740733
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2740733
• 关键词: Intra; procedural; updating; cardiac; digital

Aim of the PhD Project:To develop the next generation of cardiac digital twin models which are automatically updated and enhanced using patient intra-procedural imaging and electrophysiological mapping data from the latest novel hybrid electroanatomical mapping-imaging system, facilitated by AI.Apply our enhanced digital twin models to optimise target identification in ventricular tachycardia ablation.Project Description:Clinical Motivation: Ischemic heart disease (IHD) results in the formation of scar tissue in the heart. Scar-related arrhythmias, such as VT, are responsible for ~6 million sudden cardiac deaths annually. Often, the only effective therapy for patients suffering from incessant VT is highly-invasive catheter ablation. However, procedure times and complication rates are high, whilst success rates are punitively low: typically, >50% of patients will present with VT recurrence within 1-year post-procedure. Patient-specific computational simulation provides an exciting opportunity for integrating the wealth of existing imaging and electrical data for more accurate and beneficial pre-procedure and in-procedure planning, increasing success and reducing risk.State-of-the-art: Computational cardiology will play a leading role in the future of cardiovascular medicine as it allows integrating comprehensive multi-model imaging data and functional measurements through digital twins: in-silico replicas of a patient's heart. Currently, such models are only created pre-procedurally, and then used as static entities during invasive procedures. Automated updating from on-going clinical data acquisition is currently lacking, but is important in order to refine and augment their utility, particularly in progressive diseases such as IHD.Specific Goal: In this project, we will focus on the development of a new class of digital twins which are automatically updated from data recorded during VT ablation. Increasing the fidelity of the digital twin in this manner will allow accurate and up-to-date parameterisation of properties such as slow conduction through diastolic channels and repolarisation properties of scar border-zone tissue, both of which are known to have vital mechanistic roles in VT dynamics. Their accurate representation is essential to improve the accuracy of the in-silico model prediction. In addition to updates through functional measurements, a novel hybrid imaging-mapping system will provide imaging data which may be used to augment local structural formation within the model, potentially also identifying tissue type information (scar, healthy myocardium, border-zone), pivotal for faithful representation of VT circuits within the model.Process: Initial anatomical models will be generated from pre-procedural imaging (MRI) data, augmented from electrophysiological recordings (ECG) to embellish functional properties such as tissue conductivity and repolarisation properties. Technological workflows for updating these initial models will then be developed, using the in-procedure functional and structural data from the hybrid imaging-mapping system. Creation of initial digital twin models, and techniques for updating, will be driven by AI, utilizing simulation data for forward training, and existing extensive experimental measurements for later tuning. Simulations of VT circuits may then be performed within the models, utilising and further developing novel methods for rapid VT simulation, allowing optimal ablation targets to be identified. Performing such simulations with both initial and updated digital twin models will facilitate a direct comparison which may be used to further optimise and guide the collection of clinical data.

博士项目的目标：开发下一代心脏数字孪生模型，该模型使用最新的混合电解剖标测成像系统的患者术中成像和电生理标测数据自动更新和增强，并由人工智能提供便利。应用我们增强的数字孪生模型优化室性心动过速消融术中的靶点识别。项目描述：临床动机：缺血性心脏病（IHD）导致心脏瘢痕组织的形成。瘢痕相关的心律失常，如室性心动过速，每年造成约600万心脏性猝死。通常，对于患有持续性室性心动过速的患者，唯一有效的治疗方法是高度侵入性导管消融术。然而，手术时间和并发症发生率很高，而成功率非常低：通常，>50%的患者在术后1年内会出现VT复发。患者特定的计算模拟为整合大量现有的成像和电数据提供了一个令人兴奋的机会，以实现更准确和更有益的术前和术中计划，提高成功率并降低风险。最新技术：计算心脏病学将在心血管医学的未来发挥主导作用，因为它可以通过数字孪生模型整合全面的多模型成像数据和功能测量：病人心脏的电脑复制品目前，这种模型仅在手术前创建，然后在侵入性手术期间用作静态实体。目前缺乏从正在进行的临床数据采集自动更新，但重要的是，以完善和增强其效用，特别是在进行性疾病，如IHD.Specific目标：在这个项目中，我们将专注于开发一类新的数字双胞胎，自动更新VT消融过程中记录的数据。以这种方式增加数字双胞胎的保真度将允许准确和最新的参数化特性，例如通过舒张通道的缓慢传导和疤痕边缘区组织的复极特性，这两者都已知在VT动力学中具有重要的机械作用。它们的准确表示对于提高计算机模型预测的准确性至关重要。除了通过功能测量进行更新之外，一种新型的混合成像-映射系统将提供成像数据，其可用于增强模型内的局部结构形成，还可能识别组织类型信息（疤痕、健康心肌、边界区），是模型中VT回路忠实表现的关键。过程：将根据术前成像（MRI）数据生成初始解剖模型，并根据电生理记录（ECG）进行增强，以修饰功能特性，例如组织电导率和复极化特性。然后，将利用来自混合成像-绘图系统的术中功能和结构数据，开发更新这些初始模型的技术工作流程。初始数字孪生模型的创建和更新技术将由人工智能驱动，利用模拟数据进行正向训练，并利用现有的广泛实验测量进行后期调整。然后可以在模型内执行VT电路的模拟，利用并进一步开发用于快速VT模拟的新方法，从而允许识别最佳消融目标。使用初始和更新的数字孪生模型进行这种模拟将有助于直接比较，这可用于进一步优化和指导临床数据的收集。