  In this work, we present a novel convolutional neural net- work based method
for perfusion map generation in dynamic suscepti- bility contrast-enhanced
perfusion imaging. The proposed architecture is trained end-to-end and solely
relies on raw perfusion data for inference. We used a dataset of 151 acute
ischemic stroke cases for evaluation. Our method generates perfusion maps that
are comparable to the target maps used for clinical routine, while being
model-free, fast, and less noisy.
