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A Two-Stage Low Rank Approach for Calibrationless Dynamic Parallel Magnetic Resonance Image Reconstruction

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Abstract

Parallel magnetic resonance imaging (MRI) is an imaging technique by acquiring a reduced amount of data in Fourier domain with multiple receiver coils. To recover the underlying imaging object, one often needs the explicit knowledge of coil sensitivity maps, or some additional fully acquired data blocks called the auto-calibration signals (ACS). In this paper, we show that by exploiting the between-frame redundancy of dynamic parallel MRI data, it is possible to achieve simultaneous coil sensitivity map estimation and image sequence reconstruction. Specially, we introduce a novel two-stage approach for dynamic parallel MRI reconstruction without pre-calibrating the coil sensitivity maps nor additionally acquiring any fully sampled ACS. Numerical experiments demonstrate that, the performance of the proposed approach is better than other state-of-the-art approaches for calibrationless dynamic parallel MRI reconstruction.

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Notes

  1. Data source - http://challenge.ismrm.org/node/53

  2. One might intuitively think that the the data frame with maximum number of samples in the auto-calibration region should be the best choice, but that is not true. For instance, in the second experiment of Sect. 5, the data frame with maximum number of samples in the auto-calibration region is frame 13, while exhaustive enumeration suggests frame 2 is the best choice, and the later one leads to significantly lower reconstruction error compared with the former one.

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Acknowledgments

We would thank the authors of [17] and [16, 20, 22] for making their codes, demos and experimental datasets free for academic use.

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Authors and Affiliations

  1. Institute of Natural Sciences, Shanghai Jiao Tong University, Shanghai, China

    Likun Hou

  2. School of Biomedical Engineering and Department of Mathematics, Shanghai Jiao Tong University, Shanghai, China

    Hao Gao

  3. Institute of Natural Sciences and School of Mathematical Sciences and MOE-LSC, Shanghai Jiao Tong University, Shanghai, China

    Xiaoqun Zhang

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  1. Likun Hou
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  2. Hao Gao
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Corresponding author

Correspondence to Xiaoqun Zhang.

Additional information

Hao Gao was partially supported by the NSFC (#11405105), the 973 Program (#2015CB856004), and the Shanghai Pujiang Talent Program (#14PJ1404500). L. Hou and X. Zhang were partially supported by NSFC (#91330102) and Sino-German center grant (GZ1025), China Postdoc Science Foundation (# 2014M551392), and 973 Program (# 2015CB856004).

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Hou, L., Gao, H. & Zhang, X. A Two-Stage Low Rank Approach for Calibrationless Dynamic Parallel Magnetic Resonance Image Reconstruction. J Sci Comput 69, 1014–1032 (2016). https://doi.org/10.1007/s10915-016-0225-6

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  • DOI: https://doi.org/10.1007/s10915-016-0225-6

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