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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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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
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Google Scholar
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Google Scholar
Luo L L, Wang Y J 2020 Chin. J. Med. Phys. 37 873
Google Scholar
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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
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Google Scholar
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Google Scholar
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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
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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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