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Abstract #0315

Partial Fourier Reconstruction in Liver DWI using a Recurrent Convolutional Network

Fasil Gadjimuradov1,2, Thomas Benkert2, Marcel Dominik Nickel2, and Andreas Maier1
1Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany, 2Magnetic Resonance, Siemens Healthcare GmbH, Erlangen, Germany

Partial Fourier (PF) acquisition allows to reduce TE in single-shot echo-planar imaging in order to increase signal-to-noise ratio (SNR) in diffusion-weighted imaging (DWI). However, when applying it to motion-prone liver DWI, conventional PF reconstruction methods fail since they rely on smoothness priors of the phase. This work proposes to use an unrolled network architecture which aims to estimate a more appropriate regularization by learned recurrent convolutions. It can be shown that reconstructions produced by the network are superior in terms of quantitative measures as well as qualitative impression compared to conventional methods which tend to introduce artifacts.

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