Super-Resolution Reconstruction of Magnetic Resonance Image Based on Deep Learning

Fig. 4. Image 1 reconstruction results with different algorithms. (a) Original image; (b) Bicubic; (c) FSRCNN; (d) EDSR; (e) SRResNet; (f) proposed algorithm

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Fig. 5. Image 2 reconstruction results with different algorithms. (a) Original image; (b) Bicubic; (c) FSRCNN; (d) EDSR; (e) SRResNet; (f) proposed algorithm

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Fig. 6. Image 3 reconstruction results with different algorithms. (a) Original image; (b) Bicubic; (c) FSRCNN; (d) EDSR; (e) SRResNet; (f) proposed algorithm

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Table 1. Convolution residual block parameter setting

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Table 1. Convolution residual block parameter setting

Layer	Input	Filter	Output
Conv-RB1	$128 \times 128 \times 64$	$3 \times 3 \times 64$	$128 \times 128 \times 64$
Conv-RB2	$128 \times 128 \times 64$	$3 \times 3 \times 128$	$128 \times 128 \times 128$
Conv-RB3	$128 \times 128 \times 128$	$3 \times 3 \times 256$	$128 \times 128 \times 256$
Conv-RB4	$128 \times 128 \times 256$	$3 \times 3 \times 512$	$128 \times 128 \times 512$
Conv-RB5	$128 \times 128 \times 512$	$3 \times 3 \times 256$	$128 \times 128 \times 256$
Conv-RB6	$128 \times 128 \times 256$	$3 \times 3 \times 128$	$128 \times 128 \times 128$
Conv-RB7	$128 \times 128 \times 128$	$3 \times 3 \times 64$	$128 \times 128 \times 64$

Table 2. Image subjective evaluation form

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Table 2. Image subjective evaluation form

Grade	Absolute measurement scale	Detail	Score
1	Excellent	The best in the group	5
2	Good	Better than the average in the group	4
3	Average	Group average	3
４	Fair	Worse than the average in the group	2
５	Poor	Worse in the group	1

Table 3. Comparison of subjective evaluation values of super-resolution reconstruction methods
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Table 3. Comparison of subjective evaluation values of super-resolution reconstruction methods
MR image Bicubic FSRCNN EDSR SRResNet Proposed method
1 1.0 2.0 3.0 4.0 5.0
2 1.0 1.8 3.0 4.2 4.8
3 1.0 1.8 3.0 4.2 5.0

Table 4. Comparison of PSNR values of various super-resolution reconstruction
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Table 4. Comparison of PSNR values of various super-resolution reconstruction
MR image Bicubic FSRCNN EDSR SRResNet Proposed method
1 28.69 29.93 29.23 29.25 31.18
2 23.10 24.23 24.17 24.75 29.88
3 26.40 25.01 26.87 26.62 29.12

Table 5. Comparison of energy gradient values of various super-resolution reconstruction
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Table 5. Comparison of energy gradient values of various super-resolution reconstruction
MR image Bicubic FSRCNN EDSR SRResNet Proposed method
1 3931202 6710233 9213167 12092766 13343760
2 5533140 8946652 12582732 14374929 16688306
3 4547242 7907218 11300894 12340983 15936792

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Mengxue Pan, Ning Qu, Yeru Xia, Deyong Yang, Hongyu Wang, Wenlong Liu. Super-Resolution Reconstruction of Magnetic Resonance Image Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2217001

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