Smallest Singular Value: a metric for assessing k-space sampling patterns
Andrew T Curtis 1 and Christopher K Anand 1
Computing and Software, McMaster University,
Hamilton, Ontario, Canada
A new metric for assessing k-space sampling patterns is
presented, which analyzes the smallest singular values
(SSV) of the image reconstruction linear operator. The
SSV is described along with an efficient means of
calculation. It is compared to the gold-standard
g-factor, and ranks candidate sampling patterns very
similarly. SSV can assess patterns in seconds to
minutes, allowing for several interesting applications.
We describe one application: assessing random sampling
distributions for incoherent aliasing statistics on
uniform and poisson-disk sampling patterns are easily
computed over thousands of random patterns, and
interesting trends arise!
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