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Abstract #0534

Deep Learning-based Adaptive Image Combination for Signal-Dropout Suppression in Liver DWI

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

Signal-dropouts due to pulsation are one of the most prominent artifacts in diffusion-weighted imaging (DWI) of the liver. It can affect a significant portion of the repetitions acquired for a given slice. Instead of performing uniform averaging which might result in locally attenuated liver signal, this work proposes to train a convolutional neural network (CNN) to estimate smooth weight maps for individual repetitions. This allows to locally suppress signal-dropouts, resulting in more homogeneous liver signal while maintaining signal-to-noise ratio (SNR) in artifact-free image regions.

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