  An attention guided scheme for metal artifact correction in MRI using deep
neural network is proposed in this paper. The inputs of the networks are two
distorted images obtained with dual-polarity readout gradients. With MR image
generation module and the additional data consistency loss to the previous work
[1], the network is trained to estimate the frequency-shift map, off-resonance
map, and attention map. The attention map helps to produce better
distortion-corrected images by weighting on more relevant distortion-corrected
images where two distortion-corrected images are produced with half of the
frequency-shift maps. In this paper, we observed that in a real MRI
environment, two distorted images obtained with opposite polarities of readout
gradient showed artifacts in a different region. Therefore, we proved that
using the attention map was important in that it reduced the residual ripple
and pile-up artifacts near metallic implants.
