  The CT perfusion (CTP) is a medical exam for measuring the passage of a bolus
of contrast solution through the brain on a pixel-by-pixel basis. The objective
is to draw "perfusion maps" (namely cerebral blood volume, cerebral blood flow
and time to peak) very rapidly for ischemic lesions, and to be able to
distinguish between core and penumubra regions. A precise and quick diagnosis,
in a context of ischemic stroke, can determine the fate of the brain tissues
and guide the intervention and treatment in emergency conditions. In this work
we present UniToBrain dataset, the very first open-source dataset for CTP. It
comprises a cohort of more than a hundred of patients, and it is accompanied by
patients metadata and ground truth maps obtained with state-of-the-art
algorithms. We also propose a novel neural networks-based algorithm, using the
European library ECVL and EDDL for the image processing and developing deep
learning models respectively. The results obtained by the neural network models
match the ground truth and open the road towards potential sub-sampling of the
required number of CT maps, which impose heavy radiation doses to the patients.
