Blood Flow

Blood flow research focuses on understanding and quantifying blood movement through the circulatory system, aiming to improve diagnosis and treatment of cardiovascular and neurological diseases. Current research employs diverse computational methods, including physics-informed neural networks, Gaussian process regression, and various deep learning architectures (e.g., convolutional neural networks, transformers), to analyze blood flow from diverse imaging modalities (ultrasound, MRI, PET). These advancements enable faster, more accurate estimations of hemodynamic parameters (e.g., cerebral blood flow, blood velocity) and improved diagnostic capabilities, particularly in scenarios with limited data or noisy measurements. This work has significant implications for personalized medicine and improved clinical outcomes.