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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