Project ID BE-MI2023_03


Co Supervisor 1A School of Biomedical Engineering & Imaging SciencesWebsite

Co Supervisor 1B School of Biomedical Engineering & Imaging SciencesWebsite

Uncovering pro-arrhythmia mechanisms of Arrhythmogenic Cardiomyopathy using image-based cardiac digital twin technology 

Background & Motivation: Arrhythmogenic Cardiomyopathy (AC) is a prevalent form of structural heart disease which carries with a high risk of sudden cardiac death due to ventricular arrhythmias. Accurately identifying at risk patients for life-saving implanted defibrillator devices with non-invasive techniques remains an important clinical challenge. Furthermore, curative catheter ablation of incessant ventricular tachycardia in this patient groups presents challenges in accurately identifying and targeting the arrhythmogenic substrate. Biophysically-detailed computational modelling has the potential to provide detailed mechanistic insight regarding the arrhythmogenic processes associated with AC, to guide patient-specific non-invasive stratification of risk and ablation targeting using in silico digital twin technology.

Skills/Training: Substantial training on computational cardiac electrophysiology will be provided within the CEMRG ( itself, along with external courses/workshops (, as well as access to KCL teaching resources (Bioelectricity 3rd course taught by Dr Bishop). Opportunities to engage with clinical fellows and regularly visit EP/MRI-lab will be provided throughout.

Yr1: Construct biophysically-detailed representations of structural remodelling within AC within idealised models and conduct advanced in-silico stimulation protocols to probe specific arrhythmogenic mechanisms.

Yr2: Translate the findings from idealised models into image-based, whole heart-torso digital twin models to understand how the arrhythmogenic features at the cardiac tissue level translate into changes in the patient ECGs which may be identified in patient clinical recordings.

Yr3: Use specific advanced patient MRI imaging protocols to create digital twin models of patients undergoing ablation. Perform careful validation of model predictions regarding the arrhythmogenic substrate, compared to electro-anatomical mapping data during the procedure.

One representative publication from each co-supervisor:

MJB: Balaban, G., Halliday, B. P., Bai, W., Porter, B., Malvuccio, C., Lamata, P., et al. (2019). Scar shape analysis and simulated electrical instabilities in a non-ischemic dilated cardiomyopathy patient cohort. PLoS Computational Biology, 15(10), e1007421–18.

AR: Mendonca Costa C, Neic A, Kerfoot E, Porter B, Sieniewicz B, Gould J, Sidhu B, Chen Z, Plank G, Rinaldi CA, Bishop MJ, Niederer SA. Pacing in proximity to scar during cardiac resynchronization therapy increases local dispersion of repolarization and susceptibility to ventricular arrhythmogenesis. Heart Rhythm. 2019 Oct;16(10):1475-1483. doi: 10.1016/j.hrthm.2019.03.027. Epub 2019 Mar 29. PMID: 30930329; PMCID: PMC6774764.