  For acute ischemic stroke (AIS) patients with large vessel occlusions,
clinicians must decide if the benefit of mechanical thrombectomy (MTB)
outweighs the risks and potential complications following an invasive
procedure. Pre-treatment computed tomography (CT) and angiography (CTA) are
widely used to characterize occlusions in the brain vasculature. If a patient
is deemed eligible, a modified treatment in cerebral ischemia (mTICI) score
will be used to grade how well blood flow is reestablished throughout and
following the MTB procedure. An estimation of the likelihood of successful
recanalization can support treatment decision-making. In this study, we
proposed a fully automated prediction of a patient's recanalization score using
pre-treatment CT and CTA imaging. We designed a spatial cross attention network
(SCANet) that utilizes vision transformers to localize to pertinent slices and
brain regions. Our top model achieved an average cross-validated ROC-AUC of
77.33 $\pm$ 3.9\%. This is a promising result that supports future applications
of deep learning on CT and CTA for the identification of eligible AIS patients
for MTB.
