Cardiac digital twins are computational tools capturing key functional and anatomical characteristics of patient hearts for investigating disease phenotypes and predicting responses to therapy. When paired with large-scale computational resources and large clinical datasets, digital twin technology can enable virtual clinical trials on virtual cohorts to fast-track therapy development. Here, we present an automated pipeline for personalising ventricular anatomy and electrophysiological function based on routinely acquired cardiac magnetic resonance (CMR) imaging data and the standard 12-lead electrocardiogram (ECG). Using CMR-based anatomical models, a sequential Monte-Carlo approximate Bayesian computational inference method is extended to infer electrical activation and repolarisation characteristics from the ECG. Fast simulations are conducted with a reaction-Eikonal model, including the Purkinje network and biophysically-detailed subcellular ionic current dynamics for repolarisation. For each patient, parameter uncertainty is represented by inferring a population of ventricular models rather than a single one, which means that parameter uncertainty can be propagated to therapy evaluation. Furthermore, we have developed techniques for translating from reaction-Eikonal to monodomain simulations, which allows more realistic simulations of cardiac electrophysiology. The pipeline is demonstrated in a healthy female subject, where our inferred reaction-Eikonal models reproduced the patient's ECG with a Pearson's correlation coefficient of 0.93, and the translated monodomain simulations have a correlation coefficient of 0.89. We then apply the effect of Dofetilide to the monodomain population of models for this subject and show dose-dependent QT and T-peak to T-end prolongations that are in keeping with large population drug response data.

翻译：心脏数字孪生是计算工具，能够捕获患者心脏的关键功能和解剖特征，用于研究疾病表型并预测治疗反应。当与大规模计算资源和大型临床数据集相结合时，数字孪生技术可在虚拟队列中开展虚拟临床试验，以加速疗法开发。本文提出了一种自动化管道，基于常规获取的心脏磁共振成像数据和标准12导联心电图，实现心室解剖结构与电生理功能的个性化建模。我们利用基于CMR的解剖模型，将序列蒙特卡洛近似贝叶斯计算推断方法扩展，从ECG中推断电激活与复极化特征。通过反应-程函模型进行快速仿真，该模型包含浦肯野网络及用于复极化的生物物理精细亚细胞离子电流动力学。针对每位患者，参数不确定性通过推断群体心室模型（而非单一模型）来表征，从而使参数不确定性可传递至治疗评估。此外，我们开发了从反应-程函模型向单域模型转化的技术，从而实现对心脏电生理学更真实的仿真。该管道在一位健康女性受试者上得到验证：我们推断的反应-程函模型以0.93的皮尔逊相关系数重现了患者ECG，而转化后的单域仿真相关系数为0.89。随后，我们对该受试者的单域模型群体施加多非利特效应，结果显示QT间期与T波峰至T波末间期的剂量依赖性延长，与大规模人群药物反应数据一致。