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LANTERN: Learn analysis transform network for dynamic magnetic resonance imaging

  • * Corresponding author: Hairong Zheng

    * Corresponding author: Hairong Zheng

S. Wang and Y. Chen contributed equally to this work

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  • This paper proposes to learn analysis transform network for dynamic magnetic resonance imaging (LANTERN). Integrating the strength of CS-MRI and deep learning, the proposed framework is highlighted in three components: (ⅰ) The spatial and temporal domains are sparsely constrained by adaptively trained convolutional filters; (ⅱ) We introduce an end-to-end framework to learn the parameters in LANTERN to solve the difficulty of parameter selection in traditional methods; (ⅲ) Compared to existing deep learning reconstruction methods, our experimental results show that our paper has encouraging capability in exploiting the spatial and temporal redundancy of dynamic MR images. We performed quantitative and qualitative analysis of cardiac reconstructions at different acceleration factors ($ 2 \times $-$ 11 \times $) with different undersampling patterns. In comparison with two state-of-the-art methods, experimental results show that our method achieved encouraging performances.

    Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C35.

    Citation:

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  • Figure 1.  The proposed LANTERN network architecture for dMRI reconstruction. In (A) and (B), the blue arrow indicates forward process. The pink arrow indicates the process of back-propagation to update network parameters, where $ i $ represents the $ i - th $ iteration and $ N_i $ represents a total of $ N_i $ iterations. $ k $ expresses that the priori loop for $ k $ times and $ (i, k) $ means that in the $ i-th $ iteration, the a priori loops $ k $ times

    Figure 2.  Visual results comparison for the sensitivity to the training data size. From left to right, the reconstruction results (top line) with neural networks trained from different amounts of data based on the proposed method with 1D random sampling pattern at an acceleration factor of 4. PSNR values are given in the middle and the reconstruction error maps are presented at the bottom

    Figure 3.  The comparison of the three initialization modes of Random Gaussian, TV, DCT and LANTERN based on the proposed method with 1D Random sampling at an acceleration factor of 4. PSNR value are given under the results

    Figure 4.  The comparison of k-t SLR, D5C5 and the proposed method with 1D Random sampling at an acceleration factor of 4. PSNR value is given under the results

    Figure 5.  The comparison of k-t SLR, D5C5 and the proposed method with 1D Random sampling at an acceleration factor of 5. PSNR value is given under the results

    Figure 6.  The comparison of various methods between average quantification index of the 50 test data and acceleration factor based on 1D Random sampling

    Figure 8.  The comparison of various methods between average quantification index of the 50 test data and acceleration factor based on Radial sampling

    Figure 9.  The training and validation loss curves of the proposed model

    Figure 7.  The comparison of k-t SLR, D5C5 and the proposed method with 2D Radial sampling at an acceleration factor of 11. PSNR value is given under the results

    Table 1.  Experimental masks and acceleration factors

    $\bf{1D\ Random}$ $\bf{2D\ Radial}$
    2X 3X 4X 5X 7X 9X 11X 2X 3X 4X 5X 7X 9X 11X 15X
     | Show Table
    DownLoad: CSV

    Table 2.  Quantitative results comparison for the sensitivity to the training data size. The average quantitative indicator values of the results reconstructed for the 50 test data with the network trained from different different amount of data with 1D Random sampling pattern at an accelerated factor of 4

    1D Random4x NMSE PSNR/dB SSIM HFEN
    data50 0.0413 40.8047 0.8943 0.8333
    data60 0.0397 41.1515 0.9 0.7939
    data80 0.0388 41.3589 0.9034 0.7729
    data100 0.0385 41.4391 0.9043 0.7633
    data120 0.0386 41.4402 0.9035 0.7685
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    Table 3.  Quantitative results comparison for the sensitivity to the initialization. The average quantitative indicator values of the results reconstructed for the 50 test data with the network trained with different initialization with 1D Random sampling pattern at an accelerated factor of 4

    1D Random4x Gaussian TV DCT LANTERN
    PSNR HFEN PSNR HFEN PSNR HFEN PSNR HFEN
    AVE 39.8089 0.9459 40.5884 0.8514 40.9971 0.8064 41.4391 0.7633
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    Table 4.  Average reconstruction quantitative metrics with standard deviation of the 50 test data based on various methods with 1D Random sampling at a different accelerated factor

    Methods Random 7X Random 11X
    PSNR SSIM HFEN PSNR SSIM HFEN
    Zero-filling 29.14$ \pm $2.2 0.57$ \pm $0.04 2.73$ \pm $0.65 27.58$ \pm $2.08 0.51$ \pm $0.04 3.12$ \pm $0.74
    Kt-SLR 33.50$ \pm $2.70 0.77$ \pm $0.03 1.83$ \pm $0.53 32.44$ \pm $2.61 0.73$ \pm $0.03 2.01$ \pm $0.63
    D5C5 36.76$ \pm $2.00 0.78$ \pm $0.03 1.40$ \pm $0.33 35.22$ \pm $2.00 0.73$ \pm $0.03 1.82$ \pm $0.50
    Proposed 37.48$ \pm $2.45 0.82$ \pm $0.02 1.31$ \pm $0.36 35.40$ \pm $2.60 0.77$ \pm $0.03 1.67$ \pm $0.53
     | Show Table
    DownLoad: CSV

    Table 5.  Average reconstruction quantitative metrics with standard deviation of the 50 test data based on various methods with radial sampling at a different accelerated factor

    Methods Radial 11X Radial 15X
    PSNR/dB SSIM HFEN PSNR/dB SSIM HFEN
    Zero-filling 22.269$ \pm $1.37 0.345$ \pm $0.06 5.198$ \pm $0.72 20.153$ \pm $1.27 0.275$ \pm $0.05 5.986$ \pm $0.67
    Kt-SLR 31.961$ \pm $2.34 0.718$ \pm $0.03 2.179$ \pm $0.51 31.518$ \pm $2.36 0.707$ \pm $0.04 2.229$ \pm $0.54
    D5C5 34.954$ \pm $2.08 0.701$ \pm $0.03 1.735$ \pm $0.42 34.248$ \pm $2.04 0.677$ \pm $0.03 1.907$ \pm $0.45
    Proposed 38.874$ \pm $2.28 0.831$ \pm $0.03 1.019$ \pm $0.26 38.115$ \pm $2.23 0.808$ \pm $0.03 1.164$ \pm $0.30
     | Show Table
    DownLoad: CSV
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