  Motion has been a challenge for magnetic resonance (MR) imaging ever since
the MR has been invented. Especially in volumetric imaging of thoracic and
abdominal organs, motion-awareness is essential for reducing motion artifacts
in the final image. A recently proposed MR imaging approach copes with motion
by observing the motion patterns during the acquisition. Repetitive scanning of
the k-space center region enables the extraction of the patient motion while
acquiring the remaining part of the k-space. Due to highly redundant
measurements of the center, the required scanning time of over 11 min and the
reconstruction time of 2 h exceed clinical applicability though. We propose an
accelerated motion-aware MR imaging method where the motion is inferred from
small-sized k-space center patches and an initial training phase during which
the characteristic movements are modeled. Thereby, acquisition times are
reduced by a factor of almost 2 and reconstruction times by two orders of
magnitude. Moreover, we improve the existing motion-aware approach with a
systematic temporal shift correction to achieve a sharper image reconstruction.
We tested our method on 12 volunteers and scanned their lungs and abdomen under
free breathing. We achieved equivalent to higher reconstruction quality using
the motion-prediction compared to the slower existing approach.

Image and Video Processing;Computer Vision and Pattern Recognition