Image-level detection of arterial occlusions in 4D-CTA of acute stroke patients using deep learning.

The triage of acute stroke patients is increasingly dependent on four-dimensional CTA (4D-CTA) imaging. In this work, we present a convolutional neural network (CNN) for image-level detection of intracranial anterior circulation artery occlusions in 4D-CTA. The method uses a normalized 3D time-to-signal (TTS) representation of the input image, which is sensitive to differences in the global arrival times caused by the potential presence of vascular pathologies. The TTS map presents the time within the cranial cavity at which the signal reaches a percentage of the maximum signal intensity, corrected for the baseline intensity. The method was trained and validated on (n=214) patient images and tested on an independent set of (n=279) patient images. This test set included all consecutive suspected-stroke patients admitted to our hospital in 2018. The accuracy, sensitivity, and specificity were 92%, 95%, and 92%. The area under the receiver operating characteristics curve was 0.98 (95% CI: 0.95- 0.99). These results show the feasibility of automated stroke triage in 4D-CTA.