Perfusion Imaging via Mass Transport

Perfusion imaging (PI) quantifies blood flow through the brain parenchyma by serial imaging. The resulting quantitative measures help clinical diagnosis and clinical decision-making, for example, to assess acute strokes and brain tumors. Despite its benefits, the widespread use of PI still faces many challenges: (1) Current existing perfusion analysis approaches mostly depend on the arterial input function (AIF), while the selection procedure for AIF is not unified and is only a coarse approximation of the actual input tracer; (2) These approaches are performed on individual voxels, thereby disregarding spatial dependencies of tracer dynamics. In this talk, I will present a series of our PDE-based approaches from a mass transport perspective, to better understand the relations between tracer’s spatial-temporal transport and strokes, meanwhile avoiding the need of approximating AIF. We (1) proposed a variable-coefficient advection-diffusion PDE framework, which models the tracer transport from both optimization- and learning-based perspectives; (2) introduced constraint-free representations of physically meaningful velocity and diffusion fields in the tracer’s PDE system. Looking forward, I am excited to explore advanced, physics-informed formulations for dynamic modeling in real-world clinical settings. A key focus will be the development of interactive models capable of real-time prediction of patient outcomes following interventional treatments. By bridging theoretical foundations and practical applications, my long-term vision is to strengthen the robustness of machine learning, address real-world challenges in clinical data, and ultimately contribute to a safer, more reliable, and accessible healthcare system.