Angio-AI: Cerebral Perfusion Angiography with Machine Learning

Ebrahim Feghhi, Yinsheng Zhou, John Tran, David S. Liebeskind, Fabien Scalzo

Abstract: Angiography is a medical imaging technique used to visualize blood vessels. Perfusion angiography, where perfusion is defined as the passage of blood through the vasculature and tissue, is a computational tool created to quantify blood flow from angiography images. Perfusion angiography is critical in areas such as stroke diagnosis, where identification of areas with low blood flow and where assessment of revascularization are essential. Currently, perfusion angiography is performed through deconvolution methods that are susceptible to noise present in angiographic imaging. This paper introduces a machine learning-based formulation to perfusion angiography that can greatly speed-up the process. Specifically, kernel spectral regression (KSR) is used to learn the function mapping between digital subtraction angiography (DSA) frames and blood flow parameters. Model performance is evaluated by examining the similarity of the parametric maps produced by the model as compared those obtained via deconvolution. Our experiments on 15 patients show that the proposed Angio-AI framework can reliably compute parametric cerebral perfusion characterization in terms of cerebral blood volume (CBV), cerebral blood flow (CBF), arterial cerebral blood volume, and time-to-peak (TTP).