Cited by 161Advanced Image Processing TechniquesAdvanced MRI Techniques and ApplicationsImage and Signal Denoising Methods

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Simultaneous single-and multi-contrast super-resolution for brain MRI images based on a convolutional neural network

Zeng, Kun, Zheng, Hong, Cai, Congbo, Yang, Yu, Zhang, Kaihua, Chen, Zhong·2018·Computers in Biology and Medicine

DOI 10.1016/j.compbiomed.2018.06.010

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Abstract

In magnetic resonance imaging (MRI), the acquired images are usually not of high enough resolution due to constraints such as long sampling times and patient comfort. High-resolution MRI images can be obtained by super-resolution techniques, which can be grouped into two categories: single-contrast super-resolution and multi-contrast super-resolution, where the former has no reference information, and the latter applies a high-resolution image of another modality as a reference. In this paper, we propose a deep convolutional neural network model, which performs single- and multi-contrast super-resolution reconstructions simultaneously. Experimental results on synthetic and real brain MRI images show that our convolutional neural network model outperforms state-of-the-art MRI super-resolution methods in terms of visual quality and objective quality criteria such as peak signal-to-noise ratio and structural similarity.

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Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
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Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., & Wang, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.207 · Cited by 7,570
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
(2017) · doi:10.1109/cvpr35066.2017 · Cited by 7,284
Learning a Deep Convolutional Network for Image Super-Resolution
Dong, C., Loy, C. C., He, K., & Tang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10593-2_13 · Cited by 5,910
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Dong, C., Loy, C. C., & Tang, X. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_25 · Cited by 3,786

Show all 47 references

Research works printed but not linked: 1

Cocosco, 1997, BrainWeb: online interface to a 3D MRI simulated brain database, Neuroimage, 5, S425

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[Multimodality-based super-resolution reconstruction for routine brain magnetic resonance images].
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Authors: Zeng, K., Zheng, H., Cai, C., Yang, Y., Zhang, K., & Chen, Z.
Affiliations: Xiamen University (CN)
Corresponding author: Chen, Zhong
ORCID: Zeng, Kun
Cai, Congbo
Yang, Yu
Zhang, Kaihua
Chen, Zhong
Venue: Computers in Biology and Medicine
Publisher: Elsevier BV
Published: 2018-08
Volume: 99
Pages: 133-141
ISSN: 0010-4825 (ISSN-L)
DOI: 10.1016/j.compbiomed.2018.06.010
OpenAlex: W2808495892
Cited by: 161
References: 47
Citations by year: 2026: 9 · 2025: 23 · 2024: 22 · 2023: 30 · 2022: 28 · 2021: 24 · 2020: 15 · 2019: 8 · 2018: 2
FWCI: 6.13
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Computers in Biology and Medicine (publishedVersion)
PubMed (publishedVersion)
Funders: National Natural Science Foundation of China (61601389, 11761141010, 81671674, U1632274, 61601386)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
Keywords: MRI super-resolution, brain MRI, simultaneous reconstruction, convolutional neural networks, image reconstruction, synthetic MRI
MeSH terms: Algorithms, Brain, Humans, Magnetic Resonance Imaging, Neural Networks, Computer, Signal-To-Noise Ratio

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Simultaneous single-and multi-contrast super-resolution for brain MRI images based on a convolutional neural network

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