Abstract #0934

Deep CNN for Outlier Detection: A Complementary Tool to Low-Rank based Methods for Reducing Motion Artefacts

Mark Bydder¹, Vahid K Ghodrati^1,2, Fadil A Ali^1,2, and Peng Hu^1,2

¹Radiology, University of California Los Angeles, Los Angeles, CA, United States, ²Biomedical Physics Inter-Departmental Graduate Program, University of California Los Angeles, Los Angeles, CA, United States

Motion 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.

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