Automated Personalised 4D Heart Modelling for Disease Prediction

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...