Automated Personalised 4D Heart Modelling for Disease Prediction

心脏解剖和功能的生成统计模型具有广泛的应用，如疾病诊断，个性化医疗，生成人群和亚人群队列的计算机试验等。心脏解剖的几何深度学习方法在重建基于群体特征的三维双心室心脏解剖形状和预测心脏收缩和松弛之间的三维形状变形方面显示出了有希望的结果。到目前为止，研究主要集中在心脏周期的特定状态；然而，整个心脏运动模式在确定潜在病理方面起着至关重要的作用。为了解决当前研究中的这一重大差距，我们的目标是使用基于深度学习的方法，从标准临床心脏磁共振成像（MRI）的3D心脏解剖中开发一种新的多模态心脏运动模型。除了心脏MRI捕获的信息外，该模型还将纳入包括年龄、性别、种族、体重等个人人口统计数据和电生理数据在内的多模态信息，以准确地建模和量化它们之间的关系。为了研究不同数据模式的差异，我们打算在庞大而多样化的英国生物银行人口中开发拟议的方法。设想的心脏解剖和运动的最终4D模型取决于人口特征和电生理学，将实现几个下游任务，包括但不限于个性化医学和硅试验，以及有助于更好地了解心脏解剖、运动和病理之间的关系。该项目将为人类心脏提供一个新的多模态几何深度学习模型，该模型将心脏几何与运动相结合，并探索它们与人口统计学和电生理学的关系。反过来，这将有助于设计计算机试验，这有可能提高临床试验的效率并降低成本。计算机试验还提供了在数据中代表性不足的亚群体中模拟干预效果的可能性，这将减少从其他人群到这些群体的试验结果的虚假外推。该模式还将对个性化医疗领域作出贡献；通过模拟干预措施对受试者解剖结构的影响，可以评估和比较不同干预措施的个性化风险概况。最后，对心脏解剖、运动、人口学、电生理学和病理学之间关系的研究可以为心血管疾病的病因学提供新的见解。该项目符合EPSRC医疗技术研究主题战略的挑战1和挑战2。对于挑战1，提出的方法将解释和分析种群，以了解疾病表型的个体和种群规模变化。这种方法还将有助于开发数字双胞胎等新型预测工具，并有助于发现易感性和疾病风险的新指标。对于挑战2，该项目将有助于开发新技术，以优化患者特异性疾病预测、生物标志物识别、决策支持系统和预测疾病易感性。

Generative statistical models of cardiac anatomy and function have a wide range of applications such as disease diagnosis, personalised medicine, generation of population and sub-population cohorts for in silico trials, etc. Geometric deep learning methods for cardiac anatomy have shown promising results for reconstructing the 3D biventricular cardiac anatomy shapes conditioned on population characteristics and predicting the 3D shape deformations between heart contraction and relaxation. Until now, research has focused on specific states within the cardiac cycle; however, the whole cardiac motion pattern plays a crucial role in determining the underlying pathologies.To address this significant gap in the current research, our aim is to develop a novel multi-modal model for the complete cardiac motion over the 3D cardiac anatomy from the standard clinical cardiac magnetic resonance imaging (MRI), using deep learning-based approaches. In addition to the information captured by cardiac MRI, the proposed model will also incorporate multi-modal information including individual's demography such as age, sex, ethnicity, body mass, etc. and electrophysiology data to accurately model and quantify their relationships. We intend to develop the proposed approach on the large and diverse UK Biobank population, in order to investigate the differences in the different data modalities. The envisaged final 4D model of cardiac anatomy and motion conditioned over population characteristics and electrophysiology would enable several downstream tasks, including but not limited to personalised medicine and in silico trials, as well as contribute to a greater knowledge of the relationship among cardiac anatomy, motion and pathologies.The proposed project would contribute a novel multi-modal geometric deep-learning model for the human heart, which would integrate cardiac geometry with motion and explore their relationship with population demography and electrophysiology. This, in turn, would contribute to the design of in silico trials, which have the potential to improve the efficiency of clinical trials and reduce costs. In silico trials also offer the possibility of simulating the efficacy of interventions in sub-populations underrepresented in the data, which would reduce spurious extrapolations of trial results from other populations to these groups. The model would also contribute towards the field of personalised medicine; by simulating the impact of interventions on a bespoke simulation of a subject's anatomy, personalised risk profiles for different interventions can be evaluated and compared. Finally, investigations into the relationships between cardiac anatomy, motion, demography, electrophysiology, and pathology could yield new insights into the aetiology of cardiovascular diseases.The project fits within both Challenges 1 and 2 of the EPSRC Healthcare Technologies Research Theme strategy. For Challenge 1, the proposed approach will interpret and analyse population to understand both individual and population scale variation in disease phenotypes. The approach would also contribute towards the development of novel prediction tools like digital twins and the discovery of new indicators of susceptibility and risk of disease. For Challenge 2, the project would contribute to the development of novel techniques for optimising patient-specific illness prediction, biomarker identification, decision-support systems, and predicting susceptibility to illness.