In acute ischemic stroke treatment, prediction of tissue survival outcome plays a fundamental role in the clinical decision-making process, as it can be used to assess the balance of risk vs. possible benefit when considering endovascular clot-retrieval intervention. For the first time, we construct a deep learning model of tissue fate based on randomly sampled local patches from the hypoperfusion (Tmax) feature observed in MRI immediately after symptom onset. We evaluate the model with respect to the ground truth established by an expert neurologist four days after intervention. Experiments on 19 acute stroke patients evaluated the accuracy of the model in predicting tissue fate. Results show the superiority of the proposed regional learning framework versus a single-voxel-based regression model.

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Hyperperfusion detected on arterial spin labeling (ASL) images acquired after acute stroke onset has been shown to correlate with development of subsequent intracerebral hemorrhage. We present in this study a quantitative hyperperfusion detection model that can provide an objective decision support for the interpretation of ASL cerebral blood flow (CBF) maps and rapidly delineate hyperperfusion regions. The detection problem is solved using Deep Learning such that the model relates ASL image patches to the corresponding label (normal or hyperperfused). Our method takes into account the regional intensity values of contralateral hemisphere during the labeling of a pixel. Each input vector is associated to a label corresponding to the presence of hyperperfusion that was manually established by a clinical researcher in Neurology. When compared to the manually established hyperperfusion, the predicted maps reached an accuracy of 97.45 ± 2.49% after crossvalidation. Pattern recognition based on deep learning can provide an accurate and objective measure of hyperperfusion on ASL CBF images and could therefore improve the detection of hemorrhagic transformation in acute stroke patients.

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