The DOI matches its Crossref record; 45 of the 47 references it cites were found in a registry or an index.
Simultaneous single-and multi-contrast super-resolution for brain MRI images based on a convolutional neural network
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Recently deep learning approaches has been introduced, e.g. generating super-resolution single (no reference information) and multi-contrast (applying a high-resolution image of another modality as reference) brain MR images using CNNs [197]; constructing superresolution brain MRI by a CNN stacked by multi-scale fusion units [198]; and super-resolution musculoskeletal MRI ('DeepResolve') [199].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [197]
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Image quality assessment: from error visibility to structural similarity
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
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Enhanced Deep Residual Networks for Single Image Super-Resolution
Lim, B. O., Son, S., Kim, H., Nah, S., & Lee, K. M. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.151 · Cited by 8,117
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870
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
Accelerating the Super-Resolution Convolutional Neural Network
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
Research works printed but not linked: 1
- Cocosco, 1997, BrainWeb: online interface to a 3D MRI simulated brain database, Neuroimage, 5, S425
Cited by
Sorted by citations
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012
A Review of the Deep Learning Methods for Medical Images Super Resolution Problems
Li, Y., Sixou, B., & Peyrin, F. (2020) · IRBM · doi:10.1016/j.irbm.2020.08.004 · Cited by 295
Multi-Contrast Super-Resolution MRI Through a Progressive Network
Lyu, Q., Shan, H., Steber, C. R., Helis, C., Whitlow, C., Chan, M., & Wang, G. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2974858 · Cited by 213
Özyurt, F., Sert, E., & Avcı, D. (2020) · Medical Hypotheses · doi:10.1016/j.mehy.2019.109433 · Cited by 210
Metadata
- Authors
- Zeng, K., Zheng, H., Cai, C., Yang, Y., Zhang, K., & Chen, Z.
- Affiliations
- Xiamen University (CN)
- Corresponding author
- Chen, Zhong
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 99
- Pages
- 133-141
- ISSN
- 0010-4825 (ISSN-L)
- 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
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
