minimize respiratory motion-induced image blurring and artifacts, conventional
cardiothoracic and abdominal MRI techniques rely mostly on breath-holding.
These approaches result in limited time window for data acquisition, especially
in many ill patients who are unable to breath-hold for an extended period of
time. In this study, we employed deep learning as a promising tool for
detection and correction of complex respiratory motion during free-breathing
MRI scanning. On average, our proposed network increased the sharpness of the
images 20 percent.