is common in MR image acquisitions. It causes
artefacts in image quality that requires repeated scans, increasing the burden
for patients and providers. There have
been advances on hardware and software fronts in the quest to avoid such
issues. The latter include data consistency
methods which require no additional installation but can fail to detect high
frequency outliers in k-space. We
propose a Deep CNN approach to detect motion-corrupted phase encode lines,
coupled with a low-rank reconstruction.
This approach improves outlier detection in comparison to low-rank only
methods and accelerates reconstruction time.