  Purpose: Parallel imaging methods in MRI have resulted in faster acquisition
times and improved noise performance. ESPIRiT is one such technique that
estimates coil sensitivity maps from the auto-calibration region using an
eigenvalue-based method. This method requires choosing several parameters for
the the map estimation. Even though ESPIRiT is fairly robust to these parameter
choices, occasionally, poor selection can result in reduced performance. The
purpose of this work is to automatically select parameters in ESPIRiT for more
robust and consistent performance across a variety of exams.
  Theory and Methods: Stein's unbiased risk estimate (SURE) is a method of
calculating an unbiased estimate of the mean squared error of an estimator
under certain assumptions. We show that this can be used to estimate the
performance of ESPIRiT. We derive and demonstrate the use of SURE to optimize
ESPIRiT parameter selection.
  Results: Simulations show SURE to be an accurate estimator of the mean
squared error. SURE is then used to optimize ESPIRiT parameters to yield maps
that are optimal in a denoising/data-consistency sense. This improves g-factor
performance without causing undesirable attenuation. In-vivo experiments verify
the reliability of this method.
  Conclusion: Simulation experiments demonstrate that SURE is an accurate
estimate of expected mean squared error. Using SURE to determine ESPIRiT
parameters allows for automatic parameter selections.In-vivo results are
consistent with simulation and theoretical results.
