  For several years, numerous attempts have been made to reduce noise and
artifacts in MRI. Although there have been many successful methods to address
these problems, practical implementation for clinical images is still
challenging because of its complicated mechanism. Recently, deep learning
received considerable attention, emerging as a machine learning approach in
delivering robust MR image processing. The purpose here is therefore to explore
further and review noise and artifact reduction using deep learning for MRI.
