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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