  22. Shortening acquisition time and reducing the motion-artifact are two of
the most critical issues in MRI. As a promising solution, high-quality MRI
image restoration provides a new approach to achieve higher resolution without
costing additional acquisition time or modification on the pulse sequences.
Recently, as to the rise of deep learning, convolutional neural networks have
been proposed for super-resolution (SR) image generation and motion-artifact
reduction (MAR) for MRI. Recent studies suggest that using perceptual feature
space loss and k space loss to capture the perceptual information and
high-frequency information of images, respectively. However, the quality of
reconstructed SR and MAR MR images is limited because the most important
details of the informative area in the MR image, the edges and the structure,
cannot be very well restored. Besides, lots of the SR approaches are trained by
using low-resolution images generated by applying bicubic or blur-downscale
degradation, which cannot represent the real process of MRI measurement. Such
inconsistencies lead to performance degradation in the reconstruction of SR
images as well. This study reveals that using the L1 loss of SSIM and gradient
map edge quality loss could force the deep learning model to focus on studying
the features of edges and structure details of MR images, thus generating SR
images with more accurate, fruitful information and reduced motion-artifact. We
employed a state-of-the-art model, RCAN, as the network framework in both SR
and MAR tasks, trained the model by using low-resolution images and
motion-artifact affected images which were generated by emulating how they are
measured in the real MRI measurement to ensure the model can be easily applied
in the practical clinic environment, and verified the trained model could work
fairly well.
