Ventricular tachycardia (VT) represents an important cause of morbidity and mortality, affecting tens of thousands of people in the UK annually. Catheter ablation of VT, which aims to locate and destroy the malbehaving tissue driving this potentially lethal arrhythmia, remains a highly complex, lengthy, and high-risk procedure with poor success rates. Cardiac digital twin models, constructed from patient imaging and electrical data, have the potential to virtually simulate the clinical arrhythmia the patient is suffering from to provide enhanced guidance during the procedure of ablation targets. In this project, we will develop a computational pipeline to construct the next generation of cardiac digital twin models for pre- and intra-procedural ablation guidance. Models will be constructed from comprehensive anatomical patient imaging data, functionally calibrated by unique interrogation of their implanted cardiac electronic device. Simulated predictions of VT circuits and targets will be compared to real patient data for careful validation of predictions.
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Ventricular Tachycardia Ablation Guidance Using Cardiac Digital Twin Models Calibrated by Pre- and Intra-Procedural Implanted Device Signals
