We are interested in understanding how risk factors like endothelial shear stress (ESS) trigger complex biomechanical events leading to atherosclerotic pathologies. Difficulties of measuring ESS in vivo made these simulations crucial for unraveling and predicting cardiovascular disease progression. Our lab is applying computational, algorithmic, and physics-based advances to model blood flow in large regions of patient-specific geometries over time.
Image: The geometry of the 12.5µm resolution test case, derived from a CTA scan of human coronary arteries. The inset shows a detail of the geometry with red blood cells visible. Note: the red color in the inset is meant simply to highlight the presence of RBCs and is not an indicator of ESS. The Endothelial Shear Stress (ESS) is the ﬁeld derived from the simulations that encodes the atherosclerotic risk map and is represented as a color map on the arterial walls. From: Peters 2010.
Computational models are a potentially powerful tool for treatment planning. We are collaborating with clinicians at the Brigham and Women's Hospital in Boston to determine how personalize hemodynamic models can support clinical decision making. We recently conducted a feasbility study in which post-operative vascular candidates were tested under both resting and exercise conditions. Our objective is to use simulation to study the impact different treatment plans would have on key hemodynamic properties or risk factors and provide the capability of stratifying potential patient risk before treatment takes place.