Neural Network-derived perfusion maps: a Model-free approach to computed tomography perfusion in patients with acute ischemic stroke - Federico D'Agata; Enzo Tartaglione; Marco Grangetto; Francesca Bertolino; Ambra Santonocito; Edwin Bennink; Mauro Bergui; Umberto Amedeo Gava (Zenodo, 2021)
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. 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.
Deep-Learning and HPC to Boost Biomedical Applications for Health
Keywords: cc-by-4.0; Deep Learning; CT Perfusion; Brain; Ischemic Stroke; Cerebral Blood Flow