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Abstract #3619

Effective Rank for Automated Parallel Imaging Regularization

Stephen F Cauley 1,2 , Kawin Setsompop 1,2 , Lawrence Wald 1,2 , and Jonathan R Polimeni 1,2

1 Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, MA, United States, 2 Dept. of Radiology, Harvard Medical School, Boston, MA, United States

Regularization of parallel imaging (PI) reconstruction has a significant impact on signal-to-noise and image artifact levels. Attempts have been made to automatically determine the correct balance between stability and data consistency. We introduce effective rank as a proxy to be used for automated PI regularization. Unlike condition number, effective rank correlates with the number of dominate basis vectors that are contributing to the reconstruction. Line search algorithms can quickly sweep regularization levels to determine the appropriate parameter. We demonstrate the benefits of our approach for GRAPPA reconstruction with two classes of regularization using typical array coils and acceleration factors.

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