  Motion artifacts in Magnetic Resonance Imaging (MRI) are one of the
frequently occurring artifacts due to patient movements during scanning. Motion
is estimated to be present in approximately 30% of clinical MRI scans; however,
motion has not been explicitly modeled within deep learning image
reconstruction models. Deep learning (DL) algorithms have been demonstrated to
be effective for both the image reconstruction task and the motion correction
task, but the two tasks are considered separately. The image reconstruction
task involves removing undersampling artifacts such as noise and aliasing
artifacts, whereas motion correction involves removing artifacts including
blurring, ghosting, and ringing. In this work, we propose a novel method to
simultaneously accelerate imaging and correct motion. This is achieved by
integrating a motion module into the deep learning-based MRI reconstruction
process, enabling real-time detection and correction of motion. We model motion
as a tightly integrated auxiliary layer in the deep learning model during
training, making the deep learning model 'motion-informed'. During inference,
image reconstruction is performed from undersampled raw k-space data using a
trained motion-informed DL model. Experimental results demonstrate that the
proposed motion-informed deep learning image reconstruction network
outperformed the conventional image reconstruction network for motion-degraded
MRI datasets.
