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Efficient operator splitting algorithm for joint sparsity-regularized SPIRiT-based parallel MR imaging

Jizhong Duan1, Yu Liu2, Peiguang Jing3

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

Magnetic Resonance Imaging
|November 13, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a faster algorithm for reconstructing parallel magnetic resonance imaging (SPIRiT) using operator splitting and split Bregman methods. The new approach significantly improves reconstruction speed compared to existing methods like ADMM.

Keywords:
Auto-calibratingBarzilai and Borwein methodFISTAJoint total variationOperator splittingParallel magnetic resonance imagingSelf-consistent parallel imaging (SPIRiT)

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Area of Science:

  • Medical Imaging
  • Magnetic Resonance Imaging (MRI)
  • Computational Imaging

Background:

  • Self-consistent parallel imaging (SPIRiT) is crucial for accelerating MRI acquisition.
  • Existing reconstruction methods like POCS and NLCG have limitations in image quality, computational complexity, and convergence speed.
  • Improving the efficiency and accuracy of SPIRiT reconstruction is essential for clinical applications.

Purpose of the Study:

  • To develop a novel, faster, and more efficient algorithm for reconstructing parallel MRI data using the SPIRiT framework.
  • To address the computational demands and slow convergence of existing methods for regularized SPIRiT problems.
  • To incorporate JTV and JL1 regularization terms for enhanced image reconstruction quality.

Main Methods:

  • Formulation of the Cartesian SPIRiT problem with JTV and JL1 regularization terms.
  • Application of the operator splitting (OS) technique to decompose the problem into gradient and denoising subproblems.
  • Utilizing a split Bregman based denoising algorithm to solve the decomposed problem.
  • Employing the Barzilai and Borwein method for efficient step size updates.

Main Results:

  • The proposed algorithm demonstrates superior performance compared to the Alternating Direction Method of Multipliers (ADMM).
  • Achieved 1.3x speed improvement over ADMM for datasets with 8 channels.
  • Demonstrated a 2x speed improvement over ADMM for datasets with 32 channels.
  • Validated through simulation experiments on two in vivo datasets.

Conclusions:

  • The proposed split Bregman based algorithm offers a significant speed advantage for Cartesian SPIRiT reconstruction.
  • The method effectively handles JTV and JL1 regularization, potentially improving image quality.
  • This advancement could lead to faster MRI scans and improved diagnostic capabilities.