UniToBrain dataset: a Brain Perfusion Dataset
This work addresses the need for rapid and precise diagnosis in ischemic stroke by providing an open dataset and method to reduce patient radiation doses, though it is incremental as it builds on existing algorithms.
The authors introduced UniToBrain, the first open-source CT perfusion dataset for brain imaging, comprising over 100 patients with metadata and ground truth maps, and developed a neural network algorithm that matches ground truth results, potentially enabling reduced radiation exposure by subsampling CT maps.
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.