Perfusion parameter estimation using neural networks and data augmentation

© Springer Nature Switzerland AG 2019. 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.

ISBN:9783030117221

Publication year:2019

BOF-keylabel:yes

IOF-keylabel:yes

Authors from:Higher Education