Abstract WP11: Semi-automated Quantitative Assessment of Reperfusion in Acute Ischemic Stroke Patients

Introduction: Accurate assessment of brain reperfusion during endovascular treatment helps determining whether adjuvant intervention is necessary. The gold standard for reperfusion scales are the modified and extended Treatment in Cerebral Ischemia (mTICI and eTICI) scale. However, these scales are coarse and highly dependent on the observer, which may result in imprecise rating of reperfusion status. In this study, we developed a semi-automated quantitative reperfusion measure (qTICI) that was based on eTICI methodology using deep learning. Methods: Digital subtraction angiography (DSA) images of 67 acute ischemic stroke patients with proximal occlusions were collected from the MRCLEAN registry, including only patients who were eTICI graded by an independent core lab and had follow-up 90 days-modified Rankin Scale (mRS) scores. Both DSA projections, anteroposterior and lateral, were used. We trained a patch-based multi-path convolutional neural network to segment the perfusion area. The ground truths of perfusion segmentation were constructed using heuristic, intensity thresholding after vessel removal, which were inspected for their accuracy by a neuroradiologist. The target downstream territory was manually segmented based on the treated occlusion location by two neurointerventionalists. qTICI was formulated as a ratio between the area of perfusion and target downstream territory. We compared performance between qTICI and eTICI in relation with functional outcome, dichotomized as favorable (mRS 0-2) and unfavorable outcome (mRS 3-6). Results: Our study showed that the multi-path convolutional neural network was able to reproduce the ground truth perfusion segmentation with an average Dice score of 0.91. qTICI correlated significantly with eTICI (p Conclusions: qTICI provided a quantitative and semi-automated perfusion assessment with strong association with functional outcome.