Automated Personalised 4D Heart Modelling for Disease Prediction

心脏解剖和功能的生成性统计模型在疾病诊断、个性化医学、群体和亚群体队列的生成等方面有着广泛的应用。心脏解剖学的几何深度学习方法在根据群体特征重建三维双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良好的结果。到目前为止，研究集中在心脏周期中的特定状态；然而，整个心脏运动模式在确定潜在的病理机制中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，我们的目标是利用基于深度学习的方法，从标准的临床心脏磁共振成像(MRI)中开发一个新的多模式模型来描述3D心脏解剖中的完整心脏运动。除了心脏核磁共振获取的信息外，建议的模型还将纳入多模式信息，包括个人的人口统计信息，如年龄、性别、种族、体重等，以及电生理数据，以准确建模和量化它们之间的关系。我们打算对庞大而多样化的英国生物库群体开发拟议的方法，以调查不同数据模式的差异。设想的心脏解剖和运动的最终4D模型以人群特征和电生理学为条件，将实现几项下游任务，包括但不限于个性化医学和计算机试验，并有助于更好地了解心脏解剖、运动和病理学之间的关系。拟议的项目将为人类心脏贡献一种新颖的多模式几何深度学习模型，该模型将心脏几何与运动相结合，并探索它们与人群人口学和电生理学的关系。这反过来将有助于硅胶试验的设计，这有可能提高临床试验的效率和降低成本。在电子计算机试验中，也提供了模拟干预在数据中代表性不足的亚群中的有效性的可能性，这将减少从其他人群到这些群体的试验结果的虚假推断。该模型还将有助于个性化医学领域；通过模拟干预措施对受试者解剖的定制模拟的影响，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的关系的调查可能会对心血管疾病的病因学产生新的见解。该项目符合EPSRC医疗保健技术研究主题战略的挑战1和挑战2。对于挑战1，拟议的方法将解释和分析群体，以了解疾病表型的个体和群体规模差异。该方法还将有助于开发新的预测工具，如数字双胞胎，以及发现新的易感性和疾病风险指标。对于挑战2，该项目将有助于开发新的技术，以优化特定患者的疾病预测、生物标记物识别、决策支持系统和预测疾病的易感性。

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.