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