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Single-shot spatiotemporally-encoded magnetic resonance imaging (SPEN MRI) is a novel ultrafast MRI technology. The SPEN MRI possesses great resistance to inhomogeneous B0 magnetic field and chemical shift effect. However, it has inherently low spatial resolution, and the super-resolved reconstruction is required to improve the spatial resolution of SPEN MRI image without additional signal acquisition. Several super-resolved reconstruction methods have been proposed, but they all suffer the problems of long iterative solution time and/or aliasing artifacts residue in the reconstructed results. In this paper, a super-resolved reconstruction method is proposed for single-shot SPEN MRI based on deep neural network. In this method the simulation samples are used to train the deep neural network, and then the trained network model is adopted to reconstruct the real sampled signals. Experimental results of numerical simulation, water phantom and in vivo rat brain show that this method can quickly reconstruct a super-resolved SPEN image with no residual aliasing artifacts, and clear texture information. An appropriate number of training samples and an appropriate random noise level for training samples contribute to improving the reconstruction results.
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Keywords:
- magnetic resonance imaging /
- spatiotemporal encoding /
- super-resolved reconstruction /
- deep neural network
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图 1 本文所用SPEN MRI脉冲序列
Figure 1. Pulse sequence of SPEN MRI used in this study.
图 2 U-Net网络结构示意图
Figure 2. Diagram of U-Net network structure.
图 3 采用模拟样本训练U-Net的训练误差曲线和验证误差曲线
Figure 3. The training error curve and validation error curve of U-Net trained with simulated samples.
图 4 数值模拟结果, 红色矩形所围区域放大显示于相应图的右下角
Figure 4. Numerical simulation results. The region enclosed by the red rectangle is enlarged and displayed in the lower right corner of the corresponding figure.
图 5 不同成像序列和重建方法得到的水模图像, 红色矩形所围区域放大显示于相应图的左下角
Figure 5. Water phantom images obtained by different imaging sequences and reconstruction methods. The region enclosed by the red rectangle is enlarged and displayed in the lower left corner of the corresponding figure.
图 6 不同成像序列和重建方法得到的活体鼠脑图像
Figure 6. In vivo rat brain images obtained by different imaging sequences and reconstruction methods.
图 7 采用不同数量训练样本训练的5层U-Net网络重建的图像
Figure 7. Images reconstructed by 5-layer U-Net trained with different amount of training samples.
图 8 采用不同噪声水平样本训练的5层U-Net网络重建的水模图像
Figure 8. Water phantom images reconstructed by 5-layer U-Net trained with samples having different noise levels.
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[1] Shrot Y, Frydman L 2005 J. Magn. Reson. 172 179
Google Scholar
[2] Tal A, Frydman L 2006 J. Magn. Reson. 182 179
Google Scholar
[3] Solomon E, Avni R, Hadas R, Raz T, Garbow J R, Bendel P, Frydman L, Neeman M 2014 Proc. Nati. Acad. Sci. USA 111 10353
Google Scholar
[4] Ben-Eliezer N, Irani M, Frydman L 2010 Magn. Reson. Med. 63 1594
Google Scholar
[5] Chen Y, Li J, Qu X B, Chen L, Cai C B, Cai S H, Zhong J H, Chen Z 2013 Magn. Reson. Med. 69 1326
Google Scholar
[6] Cai C B, Dong J Y, Cai S H, Li J, Chen Y, Bao L J, Chen Z 2013 J. Magn. Reson. 228 136
Google Scholar
[7] Chen L, Li J, Zhang M, Cai S H, Zhang T, Cai C B, Chen Z 2015 Med. Image Anal. 23 1
Google Scholar
[8] Aliotta E, Nourzadeh H, Sanders J, Muller D, Ennis D B 2019 Med. Phys. 46 1581
Google Scholar
[9] Chun J, Zhang H, Gach H M, Olberg S, Mazur T, Green O, Kim T, Kim H, Kim J S, Mutic S, Park J C 2019 Med. Phys. 46 4148
Google Scholar
[10] Le M H, Chen J, Wang L, Wang Z, Liu W, Cheng K T, Yang X 2017 Phys. Med. Biol. 62 6497
Google Scholar
[11] Liu Y, Lei Y, Wang Y, Wang T, Ren L, Lin L, McDonald M, Curran W J, Liu T, Zhou J, Yang X 2019 Phys. Med. Biol. 64 145015
Google Scholar
[12] 罗伶俐, 王远军 2020 中国医学物理学杂志 37 873
Google Scholar
Luo L L, Wang Y J 2020 Chin. J. Med. Phys. 37 873
Google Scholar
[13] 王甜甜, 王慧, 朱艳春, 王丽嘉 2021 物理学报 70 228701
Google Scholar
Wang T T, Wang H, Zhu Y C, Wang L J 2021 Acta Phys. Sin. 70 228701
Google Scholar
[14] Schlemper J, Caballero J, Hajnal J V, Price A N, Rueckert D 2018 IEEE Trans. Med. Imaging 37 491
Google Scholar
[15] Shi J, Liu Q, Wang C, Zhang Q, Ying S, Xu H 2018 Phys. Med. Biol. 63 085011
Google Scholar
[16] Quan T M, Nguyen-Duc T, Jeong W K 2018 IEEE Trans. Med. Imaging 37 1488
Google Scholar
[17] Guo C L, Wu J, Rosenberg J T, Roussel T, Cai S H, Cai C B 2020 Magn. Reson. Med. 84 3192
Google Scholar
[18] Akkus Z, Galimzianova A, Hoogi A, Rubin D L, Erickson B J 2017 J. Digit. Imaging 30 449
Google Scholar
[19] Zhang J, Wu J, Chen S J, Zhang Z Y, Cai S H, Cai C B, Chen Z 2019 IEEE Trans. Med. Imaging 38 1801
Google Scholar
[20] Ronneberger O, Fischer P, Brox T 2015 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) Munich, Germany, October 5–9, 2015 p234
[21] Siddique N, Paheding S, Elkin C P, Devabhaktuni V 2021 IEEE Access 9 82031
Google Scholar
[22] Yang G, Yu S M, Dong H, Slabaugh G, Dragotti P L, Ye X J, Liu F D, Arridge S, Keegan J, Guo Y K, Firmin D 2018 IEEE Trans. Med. Imaging 37 1310
Google Scholar
[23] Liu F, Velikina J V, Block W F, Kijowski R, Samsonov A A 2017 IEEE Trans. Med. Imaging 36 527
Google Scholar
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