2X | 3X | 4X | 5X | 7X | 9X | 11X | 2X | 3X | 4X | 5X | 7X | 9X | 11X | 15X |
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.
Citation: |
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
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
Table 1. Experimental masks and acceleration factors
2X | 3X | 4X | 5X | 7X | 9X | 11X | 2X | 3X | 4X | 5X | 7X | 9X | 11X | 15X |
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 |
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 |
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 |
0.57 |
2.73 |
27.58 |
0.51 |
3.12 |
Kt-SLR | 33.50 |
0.77 |
1.83 |
32.44 |
0.73 |
2.01 |
D5C5 | 36.76 |
0.78 |
1.40 |
35.22 |
0.73 |
1.82 |
Proposed | 37.48 |
0.82 |
1.31 |
35.40 |
0.77 |
1.67 |
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 |
0.345 |
5.198 |
20.153 |
0.275 |
5.986 |
Kt-SLR | 31.961 |
0.718 |
2.179 |
31.518 |
0.707 |
2.229 |
D5C5 | 34.954 |
0.701 |
1.735 |
34.248 |
0.677 |
1.907 |
Proposed | 38.874 |
0.831 |
1.019 |
38.115 |
0.808 |
1.164 |
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