Perfusion Parameter Estimation Using Neural Networks and Data Augmentation

  • David RobbenEmail author
  • Paul Suetens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)


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.



David Robben is supported by an innovation mandate of Flanders Innovation & Entrepreneurship (VLAIO).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Medical Image Computing (ESAT/PSI)KU LeuvenLeuvenBelgium

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