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Abstract #4694

EPI artifacts reduction using deep learning

Christophe Schülke1, Karsten Sommer1, and Peter Börnert1

1Philips Research, Hamburg, Germany

The inherent speed of EPI is penalized by the calibration prescans necessary to suppress N/2 ghosts. Here, we propose a deep neural network with a novel architecture that suppresses N/2 ghosts in a post-processing step starting from magnitude images, thereby eliminating the necessity of a prescan. The proposed network achieves better results than more classical networks of the same size by taking into account the N/2 structure of ghosts. The network architecture could easily be adapted to also correct for ghosts of higher order in multishot EPI.

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