SURE-based Automatic Parameter Selection For ESPIRiT Calibration

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: Steinu0027s 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: Simulation results show Steinu0027s unbiased risk estimate to be an accurate estimator of the mean squared error. The reliability of this method is verified through in-vivo experiments. Conclusion: We demonstrate a method that leverages SURE for optimal parameter selection of ESPIRiT for the purpose of robust auto-calibration of parallel imaging.