Our computational lab at Duke University develops advanced computational methods to better understand, monitor, and treat human disease. We build large scale computational tools that combine numerical modeling, biomedical simulation, artificial intelligence (AI), and clinical applications.
A major focus of our work is developing multiscale, three dimensional vascular digital twins from CT and MRI imaging. These models combine our Adaptive Physics Refinement (APR) framework, which captures subcellular interactions such as rare cell transport, with HARVEY, our massively parallel blood flow solver capable of simulating millions of red blood cells at organ and whole body scales. Using our Longitudinal Hemodynamic Mapping (LHM) framework, we extend these simulations across millions of heartbeats, allowing us to study physiological changes over time and predict disease progression and treatment response.
We integrate physics based modeling, AI, and machine learning to accelerate simulations, discover digital biomarkers, and develop personalized clinical decision support. Our work spans cardiovascular disease, carotid artery disease, heart failure, peripheral artery disease, and cancer cell transport in the bloodstream.
The image below illustrates our multiscale vascular digital twin framework. From left to right: Adaptive Physics Refinement (APR) modeling subcellular interactions, HARVEY simulation of millions of red blood cells, and patient specific aortic blood flow predicted with HARVEY.
