Catheter ablation of ventricular tachycardia (VT) remains a significant clinical challenge. Personalised cardiac digital twin models, constructed from patient imaging data, can be used to simulate episodes of VT. If effective, these simulations may be used for pre-procedural guidance, reducing the length and risk of procedures, whilst enhancing the efficacy. To date, models are generally only personalised on an anatomical-level (from imaging data); little or no functional (electrophysiological) measurements are used which is thought to potentially limit their ability to faithfully and robustly predict ablation targets and may ultimately prevent their uptake and use in the clinic.
In this project, we will develop strategies to automatically obtain critical electrophysiological parameters from patient electrical data in order to functionally-personalise cardiac digital twin models for enhanced VT simulation and ablation planning. We will use bespoke clinical data in the form of ECGs, electrograms from implanted cardiac devices, along with intra-procedural catheter recordings. Techniques will be developed to automatically processes this data and extract key features related to the electrical excitation and recovery properties of an individual. A cohort of (anatomical) cardiac digital twin models will be made from existing clinical data. The derived electrical parameters will then be used to functionally calibrate the digital twin models. Advanced simulation technologies will be used to simulate episodes of VT in the patient models with and without functional personalisation. Simulated episodes of VT will be carefully compared to patient electrical recordings to quantitatively assess the benefit of functional personalisation. This will then be assessed in a prospective clinical study.
The successful student will develop a high level of technical skills in the curation, processing and analysis of clinical data, along with an in-depth knowledge of clinical cardiac electrophysiology. They will develop significant computational modelling skills, both in the creation of cardiac models from patient data, along with the conduction of simulations of electrophysiological function and detailed analysis.
Yr 1 – Conduct an in-depth literature review. Construct a cohort of anatomical cardiac digital twin models.
Yr2 – Obtain detailed electrophysiological recordings from patients and develop a suite of automated analysis tools to extract relevant parameters.
Yr3 – Develop strategies to functionally parameterise models and conduct simulations of VT dynamics; develop strategies for automated analysis for comparison with patient data.
Yr4 – Combine all developed tools into industry-standard software for dissemination; write-up thesis.
3-month Rotation: Focus on developing tools to extract parameters from ECG data for personalising models