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SURE-based automatic parameter selection for ESPIRiT calibration.

Siddharth Iyer1,2,3, Frank Ong1, Kawin Setsompop2

  • 1Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA.

Magnetic Resonance in Medicine
|July 21, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces automatic parameter selection for ESPIRiT (a parallel imaging method) using Stein's unbiased risk estimate (SURE). This ensures more robust and consistent coil sensitivity map estimation in MRI scans.

Keywords:
ESPIRiTStein’s unbiased risk estimateparallel imaging calibration

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Area of Science:

  • Magnetic Resonance Imaging (MRI)
  • Image Reconstruction
  • Signal Processing

Background:

  • ESPIRiT is a parallel imaging technique used for estimating coil sensitivity maps in MRI.
  • Optimal parameter selection is crucial for ESPIRiT performance but can be challenging.
  • Inconsistent parameter choices can lead to reduced image quality.

Purpose of the Study:

  • To develop an automated method for selecting optimal parameters in ESPIRiT.
  • To enhance the robustness and consistency of coil sensitivity map estimation.
  • To improve the overall performance of ESPIRiT across diverse MRI examinations.

Main Methods:

  • Leveraged Stein's unbiased risk estimate (SURE) to optimize ESPIRiT parameters in a data-driven manner.
  • Compared SURE derived from high-resolution data with SURE derived from auto-calibration region (ACS) data.
  • Proposed a soft-threshold heuristic for efficient parameter optimization, avoiding exhaustive search.

Main Results:

  • SURE derived from high-resolution data accurately estimates mean squared error, enabling automatic parameter selection.
  • Parameters minimizing SURE from ACS data closely matched optimal parameters.
  • The soft-threshold heuristic achieved comparable results to exhaustive search with improved computational efficiency.
  • In-vivo experiments confirmed the reliability of the SURE-based method.

Conclusions:

  • SURE provides an effective approach for automatic parameter selection in ESPIRiT.
  • The proposed method ensures consistent and reliable coil sensitivity map estimation.
  • In-vivo results validate the simulation and theoretical findings, demonstrating practical applicability.