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Efficient Preconditioning in Joint Total Variation Regularized Parallel MRI Reconstruction

  • Zheng Xu
  • Yeqing Li
  • Leon Axel
  • Junzhou Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9350)

Abstract

Parallel magnetic resonance imaging (pMRI) is a useful technique to aid clinical diagnosis. In this paper, we develop an accelerated algorithm for joint total variation (JTV) regularized calibrationless Parallel MR image reconstruction. The algorithm minimizes a linear combination of least squares data fitting term and the joint total variation regularization. This model has been demonstrated as a very powerful tool for parallel MRI reconstruction. The proposed algorithm is based on the iteratively reweighted least squares (IRLS) framework, which converges exponentially fast. It is further accelerated by preconditioned conjugate gradient method with a well-designed preconditioner. Numerous experiments demonstrate the superior performance of the proposed algorithm for parallel MRI reconstruction in terms of both accuracy and efficiency.

Keywords

Preconditioned Conjugate Gradient Preconditioned Conjugate Gradient Method Iteratively Reweighted Little Square Linear Convergence Rate Preconditioned Conjugate Gradient Iteration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zheng Xu
    • 1
  • Yeqing Li
    • 1
  • Leon Axel
    • 2
  • Junzhou Huang
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  2. 2.Department of RadiologyNew York UniversityNew YorkUSA

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