  We develop and evaluate a neural network-based method for Gibbs artifact and
noise removal. A convolutional neural network (CNN) was designed for artifact
removal in diffusion-weighted imaging data. Two implementations were
considered: one for magnitude images and one for complex images. Both models
were based on the same encoder-decoder structure and were trained by simulating
MRI acquisitions on synthetic non-MRI images. Both machine learning methods
were able to mitigate artifacts in diffusion-weighted images and diffusion
parameter maps. The CNN for complex images was also able to reduce artifacts in
partial Fourier acquisitions. The proposed CNNs extend the ability of artifact
correction in diffusion MRI. The machine learning method described here can be
applied on each imaging slice independently, allowing it to be used flexibly in
clinical applications.
