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Acceleration of MR parameter mapping using annihilating filter-based low rank hankel matrix (ALOHA)

Authors

  • Dongwook Lee,

    1. Department of Bio and Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST), Daejon, Republic of Korea
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    • These authors contributed equally to this work.

  • Kyong Hwan Jin,

    1. Department of Bio and Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST), Daejon, Republic of Korea
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    • These authors contributed equally to this work.

  • Eung Yeop Kim,

    1. Department of Radiology, Gachon University Gil Medical Center, Republic of Korea.
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  • Sung-Hong Park,

    1. Department of Bio and Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST), Daejon, Republic of Korea
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  • Jong Chul Ye

    Corresponding author
    1. Department of Bio and Brain Engineering, Korea Advanced Institute of Science & Technology (KAIST), Daejon, Republic of Korea
    • Correspondence to: Jong Chul Ye, Ph.D., Professor, Department of Bio and Brain Engineering, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, E-mail: jong.ye@kaist.ac.kr

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Abstract

Purpose

MR parameter mapping is one of clinically valuable MR imaging techniques. However, increased scan time makes it difficult for routine clinical use. This article aims at developing an accelerated MR parameter mapping technique using annihilating filter based low-rank Hankel matrix approach (ALOHA).

Theory

When a dynamic sequence can be sparsified using spatial wavelet and temporal Fourier transform, this results in a rank-deficient Hankel structured matrix that is constructed using weighted k-t measurements. ALOHA then utilizes the low rank matrix completion algorithm combined with a multiscale pyramidal decomposition to estimate the missing k-space data.

Methods

Spin-echo inversion recovery and multiecho spin echo pulse sequences for T1 and T2 mapping, respectively, were redesigned to perform undersampling along the phase encoding direction according to Gaussian distribution. The missing k-space is reconstructed using ALOHA. Then, the parameter maps were constructed using nonlinear regression.

Results

Experimental results confirmed that ALOHA outperformed the existing compressed sensing algorithms. Compared with the existing methods, the reconstruction errors appeared scattered throughout the entire images rather than exhibiting systematic distortion along edges and the parameter maps.

Conclusion

Given that many diagnostic errors are caused by the systematic distortion of images, ALOHA may have a great potential for clinical applications. Magn Reson Med 76:1848–1864, 2016. © 2016 International Society for Magnetic Resonance in Medicine

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