  Cardiovascular Magnetic Resonance (CMR) plays an important role in the
diagnoses and treatment of cardiovascular diseases while motion artifacts which
are formed during the scanning process of CMR seriously affects doctors to find
the exact focus. The current correction methods mainly focus on the K-space
which is a grid of raw data obtained from the MR signal directly and then
transfer to CMR image by inverse Fourier transform. They are neither effective
nor efficient and can not be utilized in clinic. In this paper, we propose a
novel approach for CMR motion artifact correction using deep learning.
Specially, we use deep residual network (ResNet) as net framework and train our
model in adversarial manner. Our approach is motivated by the connection
between image motion blur and CMR motion artifact, so we can transfer methods
from motion-deblur where deep learning has made great progress to CMR
motion-correction successfully. To evaluate motion artifact correction methods,
we propose a novel algorithm on how edge detection results are improved by
deblurred algorithm. Boosted by deep learning and adversarial training
algorithm, our model is trainable in an end-to-end manner, can be tested in
real-time and achieves the state-of-art results for CMR correction.
