My Research Journey – Ventricular Arrhythmia Localization
Shijie Zhou, Ph.D.
Assistant Profesor, Department of Chemical, Paper, and Biomedical Engineering, Miami University
Wednesday, May 8th at 11:45 am
In-Person in SMBB 2650
Shijie Zhou, Ph.D., is an Assistant Professor in the Department of Chemical, Paper and Biomedical Engineering at Miami University, Oxford, OH. He received his Ph.D. in Biomedical Engineering from Dalhousie University, Canada, in 2018, spent three years as a postdoctoral fellow in the Department of Biomedical Engineering at Johns Hopkins University, Baltimore, and joined the Miami faculty in 2021. His research has been focusing on developing a new generation of cardiac electrophysiological imaging and mapping techniques centered on AI-based personalized 3D computational heart modelling.
Abstract: Sudden cardiac arrest is one of the leading causes of death in developed countries, accounting for approximately 350,000 deaths per year in the United States. The majority of these events are caused by ventricular arrhythmias (VA). Implantable defibrillators reduce mortality in high-risk patients, but do not prevent recurrent arrhythmias. Suppression of recurrent ventricular tachycardia (VT) can be effectively accomplished with catheter ablation; more recently cardiac stereotactic body radiotherapy (cSBRT) has been shown to potentially play a role. Accurate identification of the substrate responsible for the VA is key to the success of either of these modalities. Currently, there is a lack of technology-driven mapping approaches that can non-invasively and accurately target the VT substrate location based on identified VT exit sites and circuits. There is an urgent need for innovative methodologies to guide non-invasive VT treatments accurately. To bridge this gap, we present a novel ECG-image-based mapping technique. By leveraging a data-driven model combined with a patient-specific heart digital twin, we non-invasively and accurately target the VT substrate location based on identified (1) substrate-based VT circuits (e.g. conducting channel locations) and (2) predicted VT exit sites. Furthermore, the personalized heart digital twin is applied to cardiac CT imaging and used to predict VT circuits associated with individual VT morphologies. The data-driven model is used to localize VT exit sites onto the patient-specific CT surface mesh using clinically-induced/recorded VT ECGs. Subsequently, the proposed approach establishes a spatially concordant relationship based on the predicted VT circuits and exit sites, providing complementary information to accurately target VT substrate locations.