The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE)
Open accessAbstract
In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel 1×1 CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.
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A review of deep learning-based three-dimensional medical image registration methods
Xiao, H., Teng, X., Liu, C., Li, T., Ren, G., Yang, R., Shen, D., & Cai, J. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-175 · Cited by 77
Wang, S., Zhou, X., Li, C., Wang, S., Li, Y., Tan, T., & Zheng, H. (2025) · Research · doi:10.34133/research.1029 · Cited by 29
Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols
Li, B. M., Castorina, L. V., Valdés Hernández, M. D. C., Clancy, U., Wiseman, S. J., Sakka, E., Storkey, A. J., Jaime Garcia, D., Cheng, Y., Doubal, F., et al. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.887633 · Cited by 17
Metadata
- Authors
- You, C., Cong, W., Vannier, M. W., Saha, P. K., Hoffman, E. A., Wang, G., Li, G., Zhang, Y., Zhang, X., Shan, H., Li, M., Ju, S., Zhao, Z., & Zhang, Z.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2020-01
- Volume
- 39
- Issue
- 1
- Pages
- 188-203
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2995072672
- Cited by
- 459
- References
- 65
- FWCI
- 22.68
- Type
- journal article
- Language
- en
- Open access
- green
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- You, C., Cong, W., Vannier, M. W., Saha, P. K., Hoffman, E. A., Wang, G., Li, G., Zhang, Y., Zhang, X., Shan, H., Li, M., Ju, S., Zhao, Z., & Zhang, Z. (2020). CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE). IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2019.2922960
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