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Purpose: In this study we investigate whether a Convolutional Neural Network(CNN) can generate clinically relevant parametric maps from CT perfusion datain a clinical setting of patients with acute ischemic stroke. Methods: Trainingof the CNN was done on a subset of 100 perfusion data, while 15 samples wereused as validation. All the data used for the training/validation of thenetwork and to generate ground truth (GT) maps, using a state-of-the-artdeconvolution-algorithm, were previously pre-processed using a standardpipeline. Validation was carried out through manual segmentation of infarctcore and penumbra on both CNN-derived maps and GT maps. Concordance amongsegmented lesions was assessed using the Dice and the Pearson correlationcoefficients across lesion volumes. Results: Mean Dice scores from twodifferent raters and the GT maps were > 0.70 (good-matching). Inter-raterconcordance was also high and strong correlation was found between lesionvolumes of CNN maps and GT maps (0.99, 0.98). Conclusion: Our CNN-basedapproach generated clinically relevant perfusion maps that are comparable tostate-of-the-art perfusion analysis methods based on deconvolution of the data.Moreover, the proposed technique requires less information to estimate theischemic core and thus might allow the development of novel perfusion protocolswith lower radiation dose.

Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic stroke