Application of Deep Reinforcement Learning to Predict Ablation Therapy for Atrial Fibrillation from Imaging Data

博士项目的目标：开发基于图像的心房颤动（AF）患者模型，这是最常见的心律失常。应用该模型模拟房颤的多种情况及其消融治疗终止。训练深度强化学习算法来预测最佳的患者特异性消融。使用来自同一患者的电解剖心房绘图数据验证预测。项目描述：房颤（AF）是最常见的持续性心律失常，全球约有3300万人受到影响。这种疾病与发病率和死亡率增加，发生心力衰竭和中风的风险高，因此患者住院率很高有关。仅在英国，房颤的总体经济负担就占医疗保健总费用的1%。即使是先进的一线治疗，如导管消融（CA），也是高度经验性的，长期效果不佳，大约一半的房颤患者再次接受重复治疗，这进一步增加了医疗负担。这保证了新方法的发展，可以提高CA治疗的疗效和临床结果在一个大的患者群体。该项目将应用深度强化（RL）学习与患者MR成像（提供心房结构信息）和基于mri的建模（提供功能信息）相结合，设计针对患者的CA策略，以帮助临床医生并提高治疗成功率。为了实现这一目标，将从房颤患者的MRI扫描中获得患者特异性的3D左心房（LA）模型，并用于模拟患者特异性房颤场景。然后将创建RL算法，其中消融剂在3D LA周围移动，应用CA病变来终止AF，并通过奖励政策施加的反馈进行学习。该算法将经过训练，从与潜在患者MRI数据相关的模拟中学习，并在每种情况下确定最佳的CA治疗方法。经过训练和验证后，RL算法将仅从图像中预测最佳CA治疗，为每位患者提供个性化CA治疗的快速，临床兼容的工具，并最终改善这种常见疾病在大量患者群体中的治疗。图像引导程序越来越多地用于摆脱经验性治疗和改善患者的结果。然而，即使是先进的成像系统也不能提供房颤起源的关键功能信息，基于图像的患者分层和CA引导的成功仍然是次优的。基于图像的建模可以通过对给定患者的3D LA功能的预测模拟来提供缺失的功能信息，特别是通过将MR成像获得的心房结构特征与房颤心律失常联系起来。这种方法的缺点包括：(i)在详细的3D心房模型中模拟多个AF场景需要大量的计算能力；（ii）每次将额外的患者数据集成到模型中时需要重新运行模型，这两者都使得模型在临床环境中的应用不切实际。RL的应用将有助于克服这些限制：一旦RL算法使用相关的成像和基于图像的建模数据进行训练，它将提供一个快速工具，仅基于图像为训练队列之外的患者确定最佳CA治疗，而无需运行模拟。这些预测将根据患者提供的临床数据进行验证。

Aim of the PhD Project:Develop patient image-based models of atrial fibrillation (AF), the most common arrhythmia. Apply the models to simulate multiple scenarios of AF and its termination by ablation therapy. Train deep reinforcement learning algorithms to predict the optimal patient-specific ablation. Validate the predictions using electro-anatomical atrial mapping data from the same patients. Project description:Atrial fibrillation (AF) the most common sustained cardiac arrhythmia that affects about 33 million people worldwide. The disease is associated with increased levels of morbidity and mortality, high risks of developing heart failure and stroke, and therefore very high rates of patient hospitalizations. The overall economic burden of AF amounts to 1% of total healthcare costs in the UK alone. Even advanced first-line therapies, such as catheter ablation (CA), are highly empirical and have poor long-term outcomes, with about half of AF patients returning for the repeated procedures, which further contributes to the healthcare burden. This warrant the development of novel approaches that can improve the efficacy of CA therapy and clinical outcomes in a large patient population. This project will apply deep reinforcement (RL) learning in combination with patient MR imaging (to provide structural information of the atria) and MRI-based modelling (to provide functional information) to design patient-specific CA strategies that will help clinicians and improve treatment success rates. To achieve this, patient-specific 3D left atrial (LA) models will be derived from MRI scans of AF patients and used to simulate patient-specific AF scenarios. Then a RL algorithm will be created, where an ablating agent moves around the 3D LA, applying CA lesions to terminate AF and learning through feedback imposed by a reward policy. The algorithm will be trained to learn from the simulations linked with the underlying patient MRI data and identify optimal CA therapy in each case. After the training and validation, the RL algorithm will predict the optimal CA therapy from the images only, providing a fast, clinically-compatible tool for personalising CA therapy for each patient, and ultimately improving treatment of this common disease in a large patient population. Image-guided procedures are increasingly used to move away from empirical treatments and improve the patient outcomes. However, even advanced imaging systems do not provide crucial functional information about the origins of AF, and the success of image-based patient stratification and CA guidance remains suboptimal. Image-based modelling can provide the missing functional information by predictive simulations of the 3D LA function in a given patient, particularly by linking atrial structural features obtained from MR imaging with AF arrhythmogenesis. Downsides of this approach include (i) substantial computational power needed to simulate multiple AF scenarios in detailed 3D atrial models and (ii) the need to rerun the models each time additional patient data is integrated into them, which both make the application of models in a clinical setting impractical. The application of RL will help overcome such limitations: once the RL algorithm is trained using the linked imaging and image-based modelling data, it will provide a fast tool to identify optimal CA therapy for patients outside of the training cohort based on image only, without the need to run simulations. These predictions will be validated against clinical data available from the patients.