Biomedical Flows Simulation & Multiscale Modeling Lab (BioSiMM Lab)

As the common underlying process of myocardial infarction, ischemic stroke, and venous thromboembolism, thrombosis is the leading cause of death globally. Despite being the focus of numerous studies, mechanisms driving thrombosis, specifically in disease, are not yet completely understood. Understanding why a blood clot forms under specific flow conditions and how its growth may be affected by hemodynamics, patient-specific coagulation profiles, blood biochemistry, genetics, or other systemic factors is essential to improve patient outcomes. We aim to develop new models that combine a data-driven and a physics-based approach and allow integrating experimental and clinical data into a multi-scale simulation framework.

Computational simulations allow us to augment routine medical image data and obtain functional information on an individual patient basis. Using simulations, we can compute stress on the arterial wall to assess potential wall dysfunction and remodeling or, based on the flow conditions, identify and quantify procoagulant environments to determine the risk of thrombosis. This information is typically unavailable otherwise or requires a highly invasive procedure. In the BioSiMM Lab, we develop and apply multiphysics computational models to provide novel insight into cardiovascular disease and help support clinical decision-making regarding therapy or surgical intervention.

The main characteristic of coronary artery disease (CAD) in female patients is a non-obstructive pattern, with microvascular involvement in many cases. The current gold standard for coronary assessment — coronary angiography and fractional flow reserve — targets primarily obstructive CAD. Therefore, the risk of ischemic heart disease in female patients is often underestimated. There is a need for more effective, sex-based, diagnostic approaches for non-obstructive CAD. We aim to extract critical information from image-based, multiphysics computational simulations that would lay the groundwork for developing new risk stratification metrics.

One of the main limitations to adopting computational models for routine clinical use is the high computational cost and long turnaround times. One of the BioSiMM Lab goals is to engineer new modeling approaches that allow obtaining accurate and computationally feasible solutions, facilitating clinical translation. We leverage reduced-order modeling and data-driven solutions towards this goal.