Improved parallel MR imaging using a coefficient penalized regularization for GRAPPA reconstruction

Authors

  • Wentao Liu,

    1. Beijing City Key Lab for Medical Physics and Engineering, School of Physics, Peking University, Beijing, China
    2. MRI Research Center, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
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  • Xin Tang,

    1. Beijing City Key Lab for Medical Physics and Engineering, School of Physics, Peking University, Beijing, China
    2. MRI Research Center, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
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  • Yajun Ma,

    1. Beijing City Key Lab for Medical Physics and Engineering, School of Physics, Peking University, Beijing, China
    2. MRI Research Center, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
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  • Jia-Hong Gao

    Corresponding author
    1. MRI Research Center, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
    2. Brain Research Imaging Center and Department of Radiology, The University of Chicago, Chicago, Illinois, USA
    • Beijing City Key Lab for Medical Physics and Engineering, School of Physics, Peking University, Beijing, China
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Correspondence to: Jia-Hong Gao, Ph.D., Brain Research Imaging Center, The University of Chicago, 5841 South Maryland, MC 2026, Chicago, IL 60637. E-mail: jgao@uchicago.edu

Abstract

A novel coefficient penalized regularization method for generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction is developed for improving MR image quality. In this method, the fitting coefficients of the source data are weighted with different penalty factors, which are highly dependent upon the relative displacements from the source data to the target data in k-space. The imaging data from both phantom testing and in vivo MRI experiments demonstrate that the coefficient penalized regularization method in GRAPPA reconstruction is able to reduce noise amplification to a greater degree. Therefore, the method enhances the quality of images significantly when compared to the previous least squares and Tikhonov regularization methods. Magn Reson Med 69:1109–1114, 2013. © 2012 Wiley Periodicals, Inc.

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