Personalised 3D modelling of the right ventricle as an approach to ARVC

1 Abstract
Arrhythmogenic Right Ventricular Cardiomyopathy (ARVC) is one of the leading causes of Sudden Cardiac Death in young individuals. Finer stratification and early diagnosis are vital for reducing the number of premature deaths caused by ARVC. This study aims to develop personalised high performance computer models based on electrocardiographic imaging for risk stratification in ARVC. Deeper insight ARVC on the human heart will be explored via coupled simulations of ionic and structural remodelling, by solving coupled systems of ODEs/PDEs, namely the mono and bi-domain models. Fractional diffusion models will also be implemented, as they have been proven an innovative way of approximating physiological dynamics.
2 Aims and Objectives
The main goal of the study is to provide mechanistic insight into the role of the underlying, pro-arrhythmic, electro-anatomical substrate in governing the initiation and maintenance of ventricular arrhythmias in ARVC. The ultimate goal is to propose novel treatments as well as improve the efficiency of existing ones(drugs, ablation and implantable defibrillators). In order to achieve this, we will create models incorporating coupled PDEs and stiff ODEs, based on human ventricular meshes of many millions of elements.The personalised computer models will provide a basic science and diagnostic tool for transforming the raw patient data into a 3D dynamical heart representations. Electro-cardiographic imagining (ECGi) data will be central to this study, as its high spatial resolution will be used to track complex dynamics of arrhythmias.
3 Novelty of the research
There is confusion in literature as to whether electrical imbalances predate structural disarrangements in the ventricles and what causes the onset of symptoms. In this study, we aim to provide more extensive, big data and modelling backed answers. Furthermore, we want to infer if particular forms of structural disarrangement are more harmful than others. The ECGi technique is a novel, non-invasive approach to monitoring the electrical activity of the heart, boasting very high spatial resolution (4-7mm) and quick recording of data. Only 12 hospitals worldwide have access to this technique, a fraction of which use it for research purposes. The remodelling aims to create a detailed, patient specific representation of the ventricles. Inter-cell variability will be accounted for by incorporating biological data. An accurate representation of the electrical dynamics in regions, plagued by micro disarrangements, requires a fine discretisation of the relevant PDEs. This highly detailed representation of the ventricular electrical dynamics requires vast computational power. The largest super-computer in the UK (ARCHER) will be used to perform the computation. Fractional diffusion models are another approach when characterising heterogeneous electrical signals, such as those in heart arrhythmias. In order to reduce possible inaccuracies in terms of model parameterasation of physical quantities, machine learning methods will be used on patient data in order to determine realistic values for the biological parameters.
4 Alignment with EPSRCs strategies
The project falls under the Health care technologies research scheme, more specifically to the Clinical Technologies sub unit. It is a modelling and basic science project, aimed to enhance understanding in areas of biomedical interest (Techniques for Biomedical Understanding) and in the design and development of technologies for advancing the detection and diagnosis of health conditions (Diagnostics).
5 Collaborators
A close research collaboration is established with prominent cardiologists at the Barts Heart Centre in London. They will provide the ECGi recording of a cohort of 100 patients suffering from various degrees of ARVC. In addition, they will inform of the clinical challenges and needs
when diagnosing and treating patients with ARVC.