Awesome Medical Image SR

We collected many awesome medical image super-resolution (SR) methods and common benchmarks for medical image SR in this repository.

0. Review of medical image SR

[1] Van Reeth, E., Tham, I. W., Tan, C. H., & Poh, C. L. (2012). Super‐resolution in magnetic resonance imaging: a review. Concepts in Magnetic Resonance Part A, 40(6), 306-325. (paper)

1. Medical image SR before deep learning

[1] T. M. Lehmann, C. Gonner, K. Spitzer, Addendum: B-spline interpolation in medical image processing, IEEE T. Med. Imaging , 2001, (paper, [codes])

[2] Carmi, E., Liu, S., Alon, N., Fiat, A., & Fiat, D. (2006). Resolution enhancement in MRI. Magnetic resonance imaging, 24(2), 133-154. (paper, [codes])

[3] Manjón, J. V., Coupé, P., Buades, A., Fonov, V., Collins, D. L., & Robles, M. (2010). Non-local MRI upsampling. Medical image analysis, 14(6), 784-792. (paper, codes)

[4] Shi, F., Cheng, J., Wang, L., Yap, P., & Shen, D. (2015). LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations. IEEE Transactions on Medical Imaging, 34(12), 2459–2466.(paper, codes)

[5] Huang, Yawen, Ling Shao, and Alejandro F. Frangi. "Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images Using Weakly-Supervised Joint Convolutional Sparse Coding."，CVPR (2017): 5787-5796. (paper,[codes])

[6] Wei, S., Zhou, X., Wu, W., Pu, Q., Wang, Q., & Yang, X. (2018). Medical image super-resolution by using multi-dictionary and random forest. Sustainable Cities and Society,, 358-370. (paper)

2. CNN-based Medical Image SR

[1] Tanno, R., Worrall, D. E., Ghosh, A., Kaden, E., Sotiropoulos, S. N., Criminisi, A., & Alexander, D. C. (2017). Bayesian image quality transfer with CNNs: Exploring uncertainty in dMRI super-resolution. MICCA2017. (paper)

[2] Pham, C., Ducournau, A., Fablet, R., & Rousseau, F. (2017). Brain MRI super-resolution using deep 3D convolutional networks., ISBI2017. (paper,)

[3] J. Shi, Z. Li, S. Ying, C. Wang, Q. Liu, Q. Zhang, and P. Yan, “Mr image super-resolution via wide residual networks with fixed skip connection,” IEEE journal of biomedical and health informatics, vol. 23, no. 3, pp. 1129–1140, 2018.(paper)

[4] X. Xue, Y. Wang, J. Li, Z. Jiao, Z. Ren, and X. Gao, “Progressive sub-band residual-learning network for mr image super resolution,” IEEE journal of biomedical and health informatics, vol. 24, no. 2, pp. 377–386, 2019 (paper)

[5] Zhao, X., Zhang, Y., Zhang, T., & Zou, X. (2019). Channel Splitting Network for Single MR Image Super-Resolution. TIP, 28(11), 5649-5662.(paper)

[6] Zhao, X., Zhang, H., Liu, H., Qin, Y., Zhang, T., & Zou, X. (2019). Single MR Image Super-Resolution via Channel Splitting and Serial Fusion Network., arXiv, 2019. (paper)

[7] Cherukuri, V., Guo, T., Schiff, S. J., & Monga, V. (2020). Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors. TIP,, 1368-1383. (From ICIP2018 paper)

[8] Liu, K., Ma, Y., Xiong, H., Yan, Z., Zhou, Z., Fang, P., & Liu, C. (2019). Medical image super-resolution method based on dense blended attention network., arXiv, 2019,. (paper)

[9] Cherukuri, V., Guo, T., Schiff, S. J., & Monga, V. (2019). Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors. IEEE Transactions on Image Processing, 29, 1368-1383.(paper)

[10] L. Chen. , X. Yang. , G. Jeon. , M. Anisetti. , & K. Liu, (2020). A trusted medical image super-resolution method based on feedback adaptive weighted dense network., Artificial Intelligence in Medicine. ,2020,.(paper,codes)

[11] Q. Lyu, H. Shan and G. Wang, "MRI Super-Resolution With Ensemble Learning and Complementary Priors," in IEEE Transactions on Computational Imaging, vol. 6, pp. 615-624, 2020, doi: 10.1109/TCI.2020.2964201.(paper)

[12] T. Song, S. R. Chowdhury, F. Yang and J. Dutta, "Super-Resolution PET Imaging Using Convolutional Neural Networks," in IEEE Transactions on Computational Imaging, vol. 6, pp. 518-528, 2020, doi: 10.1109/TCI.2020.2964229.(paper)

[13] Zhao, X., Hu, X., Liao, Y., He, T., Zhang, T., Zou, X., & Tian, J. (2020). Accurate MR image super-resolution via lightweight lateral inhibition network. Computer Vision and Image Understanding, 201, 103075. (paper)

3. GAN-based Medical Image SR

[1] Chen, Y., Shi, F., Christodoulou, A. G., Xie, Y., Zhou, Z., & Li, D. (2018). Efficient and Accurate MRI Super-Resolution Using a Generative Adversarial Network and 3D Multi-level Densely Connected Network. MICCA2018 . (Papar,codes)

[2] Mahapatra, D., Bozorgtabar, B., & Garnavi, R. (2019). Image super-resolution using progressive generative adversarial networks for medical image analysis., Computerized Medical Imaging and Graphics,, 30-39

[3] Zhu, J., Yang, G., & Lio, P. (2018). Lesion Focused Super-Resolution., *arXiv,2018. (paper)

[4] Zhu, J., Yang, G., & Lio, P. (2019). How Can We Make Gan Perform Better in Single Medical Image Super-Resolution? A Lesion Focused Multi-Scale Approach. ISBI2019. (paper)

4. Common Benchmarks

Dataset	Web Site
OASIS	https://www.oasis-brains.org/.
ADNI	http://adni.loni.usc.edu/.
IXI	http://brain-development.org/ixi-dataset/.
Lumbar Spine MRI Dataset (LSMRI)	http://dx.doi.org/10.17632/k57fr854j2.2#file-1bc6b195-27fc-4ac4-ae43-aaa6bf386912.
MRnet Dataset	https://stanfordmlgroup.github.io/projects/mrnet/

5. Awesome-Repositories

1. Awesome GAN for Medical Imaging