I am currently leading the Image-Guided Therapy and Robotics research group at the Siemens Healthineers Digital Technology and Innovation center, led by Dorin Comaniciu, (Princeton, NJ, USA). My research focus covers artificial intelligence, computational physiology and robotics, with the goal to develop solutions to enable next generation image-guided procedures.I graduated in 2010 from INRIA Sophia Antipolis, France.
With the team, we won the R&D 100 award for Siemens eSie Valves, recognized among the top 100 innovative products of 2015. We had the honor to win the 2015 NJ Thomas Alva Edison Patent Award for our work on personalized simulation of mitral valve interventions, and the 2019 NJ Thomas Edison Patient Award, for our work on patient-specific simulation of cardiac electrophysiology. Our research also led to three MICCAI Young Scientists Awards in 2011 (Towards Patient-Specific Finite-Element Simulation of Mitral Clip Procedure), 2013 (Fast data-driven calibration of a cardiacelectrophysiology model from images and ECG) and 2018 (Task Driven Generative Modeling for Unsupervised Domain Adaptation: Application to X-ray Image Segmentation). I also had the honor to present our work at the prestigious College de France in Paris in 2019.
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!
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!
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!