Automatic Estimation of Arterial Input Function in Digital Subtraction Angiography

Abstract

Estimation of cerebral blood flow (CBF) from digital subtraction angiogram (DSA) is typically obtained through deconvolution of the contrast concentration time-curve with the arterial input function (AIF). Automatic detection of the AIF through analysis of angiograms could expedite this computation and improve its accuracy by allowing fully automated angiogram processing. This optimization is decisive given the significance of CBF modeling in diagnosing and treating cases of acute ischemic stroke, arteriovenous malformation, brain tumor, and other deviations in cerebral or renal perfusion, for example. This study presents an AIF detection model that relies on the identification of the intracranial carotid artery (ICA) through image segmentation. A contrast agent is used to detect the presence of blood flow in the angiogram, which facilitates signal intensity monitoring throughout 20 frames, ultimately allowing us to compute the AIF. When compared to the manually outlined AIF, the predicted model reached an AUROC value of \(98.54\%\). Automatic AIF detection using machine learning methods could therefore provide consistent, reproducible, and accurate results that could quantify CBF and allow physicians to expedite more informed diagnoses to a wide variety of conditions altering cerebral blood flow.