Best Presentation Award at MICCAI – STACOM 2020

Heartfelt congratulations to Felix Meister and team for winning the best oral presentation award at the STACOM 2020 workshop, a satellite event of MICCAI 2020. The award was given for his work on “Graph convolutional regression of cardiac depolarization from sparse endocardial maps”.

In this paper, we show how geometric deep learning can be used to extrapolate electrophysiology signals throughout the myocardium given sparse and noisy endocardial measurements. A big thank you to our collaborators from JHU (Baltimore, MD), Dr. Ashikaga and Dr. Halperin, as well as the Pattern Recognition Lab at FAU (Erlangen, Germany). Congratulations to the entire team!

The paper can be accessed here: https://arxiv.org/abs/2009.14068.

Check out our new book on “AI for Computational Modeling of the Heart”

Our new book presents recent research developments towards streamlined and automatic estimation of the digital twin of a patient’s heart by combining computational modeling of heart physiology and artificial intelligence. The book first introduces the major aspects of multi-scale modeling of the heart, along with the compromises needed to achieve subject-specific simulations. Reader will then learn how AI technologies can unlock robust estimations of cardiac anatomy, obtain meta-models for real-time biophysical computations, and estimate model parameters from routine clinical data. Concepts are all illustrated through concrete clinical applications.

The content of the book is the result of many years of research and hard work from the team, pushing the limit of personalized computational modeling of the heart. So proud of the team! Congratulations to everyone for this achievement! I hope you will enjoy reading the as much as we enjoyed writing it!

New Case Report: Intra-procedural evaluation of Computational Modeling Method for CRT

Buliard et al., Intra-procedural evaluation of a computational modelling method for cardiac resynchronization therapy, EP Europace, Volume 22, Issue 4, April 2020, Page 656, https://doi.org/10.1093/europace/euz239

With the team we developed a way to measure response to cardiac resynchronization therapy (CRT). CRT is a common tool used in cardiac electrophysiology (CEP), but it relies on perfect placement of the left ventricular (LV) lead. Our research team has been working on a project that evaluates how simulations can be incorporated into CEP for better lead implantation. Our case report, published in EP Europace, explores the guidance of LV lead implantation using intra-procedural CEP simulations.

To do this, we first made a computational model of CEP based on each individual patient’s cardiac MRI and ECG about one day before the CRT. During the procedure, we implanted a CRT system with a quadripolar LV lead. Using each patient’s individual data, we updated the model. We were able to sense ventricular delays and epicardial activation. We changed LV lead positions and AV delays to evaluate 6 different CRT protocols. We then simulated CRT.

We found that there was excellent agreement between the actual QRS duration and the QRS duration we had predicted. These results show that it could be possible to use computational simulations of CEP intraprocedurally to guide CRT!