  Perfusion imaging plays a crucial role in acute stroke diagnosis and
treatment decision making. Current perfusion analysis relies on deconvolution
of the measured signals, an operation that is mathematically ill-conditioned
and requires strong regularization. We propose a neural network and a data
augmentation approach to predict perfusion parameters directly from the native
measurements. A comparison on simulated CT Perfusion data shows that the neural
network provides better estimations for both CBF and Tmax than a state of the
art deconvolution method, and this over a wide range of noise levels. The
proposed data augmentation enables to achieve these results with less than 100
datasets.
