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Accelerated MR diffusion tensor imaging using distributed compressed sensing

Authors

  • Yin Wu,

    1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
    2. Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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  • Yan-Jie Zhu,

    1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
    2. Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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  • Qiu-Yang Tang,

    1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
    2. Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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  • Chao Zou,

    1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
    2. Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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  • Wei Liu,

    1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
    2. Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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  • Rui-Bin Dai,

    1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
    2. Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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  • Xin Liu,

    1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
    2. Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China
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  • Ed X. Wu,

    1. Laboratory of Biomedical Imaging and Signal Processing, The University of Hong Kong, Pokfulam, Hong Kong, China
    2. Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, China
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  • Leslie Ying,

    1. Department of Biomedical Engineering, University at Buffalo, The State University of New York, Buffalo, New York, USA
    2. Department of Electrical Engineering, University at Buffalo, The State University of New York, Buffalo, New York, USA
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  • Dong Liang

    Corresponding author
    1. Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Key Laboratory for MRI, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
    2. Key Laboratory of Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, China
    • Correspondence to: Dong Liang, Ph.D., Paul C. Lauterbur Research Centre for Biomedical Imaging, Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China. E-mail: dong.liang@siat.ac.cn

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Abstract

Purpose

Diffusion tensor imaging (DTI) is known to suffer from long acquisition time in the orders of several minutes or even hours. Therefore, a feasible way to accelerate DTI data acquisition is highly desirable. In this article, the feasibility and efficacy of distributed compressed sensing to fast DTI is investigated by exploiting the joint sparsity prior in diffusion-weighted images.

Methods

Fully sampled DTI datasets were obtained from both simulated phantom and experimental heart sample, with diffusion gradient applied in six directions. The k-space data were undersampled retrospectively with acceleration factors from 2 to 6. Diffusion-weighted images were reconstructed by solving an l2-l1 norm minimization problem. Reconstruction performance with varied signal-to-noise ratio and acceleration factors were evaluated by root-mean-square error and maps of reconstructed DTI indices.

Results

Superiority of distributed compressed sensing over basic compressed sensing was confirmed with simulation, and the reconstruction accuracy was influenced by signal-to-noise ratio and acceleration factors. Experimental results demonstrate that DTI indices including fractional anisotropy, mean diffusivities, and orientation of primary eigenvector can be obtained with high accuracy at acceleration factors up to 4.

Conclusion

Distributed compressed sensing is shown to be able to accelerate DTI and may be used to reduce DTI acquisition time practically. Magn Reson Med 71:763–772, 2014. © 2013 Wiley Periodicals, Inc.

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