  Purpose of this study was to evaluate the effect of a robust magnetic
resonance reconstruction pipeline equipped with a deep convolutional neural
network on the overall image quality, in terms of Gibbs artifact reduction, and
SNR improvement. Sixteen (16) healthy volunteers enrolled in this study and
were imaged at 3T. Representative images of each image series that were
reconstructed through the pipeline that leverages a deep learning (DL)
algorithm were retrospectively benchmarked against corresponding images
reconstructed through a conventional pipeline. DL-reconstructed images showed
significant SNR improvements compared to the corresponding conventionally
reconstructed images. In addition to that, Gibbs artifacts were effectively
eliminated, when the raw data were reconstructed through the DL pipeline. Gibbs
artifact reduction was qualitatively assessed by two experienced medical
physicists and two experienced radiologists. DL-based reconstruction can lead
to an SNR surplus which can be further invested into either higher spatial
resolution and thinner slices, or into shorter scan times.
