The goal of our research is to develop beyond state-of-the-art imaging for a better, more effective diagnosis of heart disease. We combine techniques from bioengineering, computer vision and artificial intelligence with concepts used in the field of nonlinear physics and the physics of complex biological systems to study the heart's highly dynamic behavior and bridge the gap between basic cardiovascular science, high-resolution imaging and numerical modeling.

Cardiac Arrhythmia Mechanisms

With our research, we hope to contribute to a better understanding of the underlying causes of cardiac arrhythmias and to provide new insights into ways how to cure them, from finding better ablation strategies to the development of novel anti-arrhythmic therapies. Heart rhythm disorders manifest on the organ scale as disorganized electromechanical wave phenomena, but they often originate from biophysical processes acting on the cellular or subcellular scale. We aim to link these microscopic processes with the emergent phenomena arising on the tissue scale. In our lab, we study arrhythmia dynamics by imaging transmembrane voltage, calcium and mechanical deformation in experimental model systems, and by simulating such electromechanical phenomena in computer simulations. 

Ultrasound-based Mapping of Arrhythmias

We develop a novel ultrasound-based imaging technique for diagnosing and visualizing cardiac arrhythmias and other heart disease, and aim to establish it as a routinely, clinically used imaging technique. Ultrasound-based mapping could have several advantages and could be used complementarily to conventional catheter-based mapping to better diagnose heart rhythm disorders in the EP lab. Moreover, it could be used to guide ablation, cardiac resynchronization or other forms of therapy. By combining 4D ultrasound with numerical modeling and artificial intelligence (AI), we aim to provide novel visualizations of the complex dynamics of the arrhythmic heart. AI will play a key role, as it will allow unprecedented data processing capabilities. For example, we have recently demonstrated that AI can compute electrophysiological wave phenomena from cardiac tissue deformation (Link) and can help to interpolate catheter mapping data (Link). In the future, we aim to apply AI to advance the non-invasive imaging of heart rhythm disturbances in patients.

Coupling between Cardiac Electrophysiology and Mechanics

The heart's behavior is largely determined by the mutual coupling between its electrophysiology and heart muscle mechanics. Using numerical or data-driven modeling and computer simulations in conjunction with high-resolution imaging and wetlab experiments, we aim to study the interactions and feedback phenomena between electrophysiology and soft-tissue mechanics, as well as tissue structure and function in vivo, ex vivo, in vitro and in silico. We aim to study specific phenomena such as 3D activation dynamics, mechano-sensitivity and mechano-electric feedback, and their role in acute and chronic heart disease. 

See our publications for more details.

We are part of the Cardiovascular Research Institute and collaborate closely with our colleagues from the Division of Cardiology and the Department of Bioengineering and Therapeutic Sciences at UCSF, with the ultimate goal to translate our discoveries to clinical application.