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Abstract #3460

Sparse-Coding Regularized QSM Reconstruction for Suppressing Motion Artifacts

Jingjia Chen1 and Chunlei Liu1,2
1EECS, University of California, Berkeley, Berkeley, CA, United States, 2Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, United States

Subject motion downgrades QSM image quality and accuracy and can even nullify the image for diagnostic purposes in clinical settings. While QSM plays an emerging role in evaluating neurodegenerative diseases, motion artifact reduction is crucial for its adoption by researchers and clinicians. In this project, we develop a sparse-coding regularized QSM reconstruction algorithm to mitigate motion artifacts and noise. In vivo experiments suggest that the proposed method can alleviate motion artifacts to a certain extent while preserving sharp structures. This regularization technique can be applied jointly with other regularizations to achieve a desired susceptibility map.

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