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.
翻译:CT渗漏(CTP)是一种医学检查,用来测量通过大脑通过像素等像素/象素的对比溶液的博体的通过。目的是为缺血性损伤快速绘制“聚变图”(即脑血量、脑血流和峰值时间),并能够区分核心和成膜膜区域。精确和快速的诊断,在非化学中风的情况下,可以确定脑组织的命运,指导紧急情况下的干预和治疗。在这项工作中,我们介绍了UniTobrain数据集,这是CTP的第一个开源数据集。它由100多名患者组成,配以最新算法获得的病人元数据和地面真象图。我们还提出一个新的神经网络算法,使用欧洲图书馆ECVL和EDDL分别用于图像处理和开发深层学习模型。神经网络模型取得的结果与地面真理相匹配,并打开道路向潜在的重辐射量的病人开放,需要的辐射量的份量的份数。