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Non-cartesian MRI reconstruction with automatic regularization Via Monte-Carlo SURE.

Sathish Ramani1, Daniel S Weller, Jon-Fredrik Nielsen

  • 1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109 USA. sramani@umich.edu

IEEE Transactions on Medical Imaging
|April 18, 2013
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Summary

This study introduces a new data-driven method for optimizing regularization parameters in magnetic resonance imaging (MRI) reconstruction. The approach improves image quality by minimizing artifacts in undersampled k-space data.

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Area of Science:

  • Medical Imaging
  • Computational Imaging
  • Signal Processing

Background:

  • Magnetic resonance image (MRI) reconstruction from undersampled k-space data necessitates regularization to mitigate noise and aliasing artifacts.
  • Effective regularization hinges on the precise selection of regularization parameters, a challenging aspect in MRI reconstruction.

Purpose of the Study:

  • To develop a data-driven scheme for automatic adjustment of regularization parameters in MRI reconstruction.
  • To minimize a weighted squared-error measure in k-space using an estimate based on Stein's unbiased risk estimate (SURE).

Main Methods:

  • A Monte-Carlo scheme is proposed to compute the SURE-type estimate for complex-valued images, extending previous work on inverse problems.
  • The method is algorithm-agnostic and regularizer-agnostic, applicable to various reconstruction algorithms and nonquadratic regularizers like total variation and l1-norm.
  • The approach relies solely on the output of the reconstruction algorithm, not its internal details.

Main Results:

  • The proposed SURE-based method effectively determines regularization parameters for MRI reconstruction.
  • Experiments with simulated and real MR data demonstrate the capability of the approach.
  • Near mean squared-error optimal regularization parameters were achieved for single-coil undersampled non-Cartesian MRI reconstruction.

Conclusions:

  • The developed data-driven regularization parameter adjustment scheme offers a robust solution for improving MRI reconstruction quality.
  • This approach enhances the reliability and accuracy of MRI reconstruction, particularly for undersampled non-Cartesian data.
  • The method's independence from reconstruction algorithm specifics broadens its applicability across diverse MRI techniques.