Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
530-02-003 ISMRM Abstract

Wavelet-Guided Deep Residual Network for Unsupervised Medical Image Registration

Primary: Analysis Methods - Image Registration and Alignment
Secondary: Analysis Methods - Data Processing
530-02-003 · Registration, Atlases, and Motion · Wednesday, 13 May, 8:20 AM–10:10 AM · Meeting Room 2.60
Keywords: Magnetic resonance imaging Wavelet transform Deep residual learning Deformable image registration
Accepted
Zhengyong Huang1,2, Ning Jiang1,2, Yao Sui 1,2,3
1National Institute of Health Data Science, Peking University, Beijing, China
2Institute of Medical Technology, Peking University Health Science Center, Beijing, China
3Institute for Artificial Intelligence, Peking University, Beijing, China
Presenting Author: Yao Sui

Synopsis

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References

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2. Avants, B. B., Epstein, C. L., Grossman, M., & Gee, J. C. (2008). Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical image analysis, 12(1), 26-41.
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4. Lv, J., Wang, Z., Shi, H., Zhang, H., Wang, S., Wang, Y., & Li, Q. (2022). Joint progressive and coarse-to-fine registration of brain MRI via deformation field integration and non-rigid feature fusion. IEEE Transactions on Medical Imaging, 41(10), 2788-2802.
5. Jia, X., Bartlett, J., Chen, W., Song, S., Zhang, T., Cheng, X., ... & Duan, J. (2023, June). Fourier-net: Fast image registration with band-limited deformation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 1, pp. 1015-1023).
6. Cheng, X., Jia, X., Lu, W., Li, Q., Shen, L., Krull, A., & Duan, J. (2024, October). Winet: Wavelet-based incremental learning for efficient medical image registration. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 761-771). Cham: Springer Nature Switzerland.
7. Tan, Z., Zhang, L., Lv, Y., Ma, Y., & Lu, H. (2024). GroupMorph: medical image registration via grouping network with contextual fusion. IEEE Transactions on Medical Imaging, 43(11), 3807-3819.
8. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
9. Huang, Z., Zou, S., Wang, G., Chen, Z., Shen, H., Wang, H., ... & Hu, Z. (2022). ISA-Net: Improved spatial attention network for PET-CT tumor segmentation. Computer Methods and Programs in Biomedicine, 226, 107129.
10. Wang, H., Ni, D., & Wang, Y. (2024). Recursive deformable pyramid network for unsupervised medical image registration. IEEE Transactions on Medical Imaging, 43(6), 2229-2240.
11. Huang, Z., & Sui, Y. (2025, April). Dual-Stream Pyramidal Attention Networks for Medical Image Registration. In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI) (pp. 1-5). IEEE.
12. Marcus, D. S., Wang, T. H., Parker, J., Csernansky, J. G., Morris, J. C., & Buckner, R. L. (2007). Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of cognitive neuroscience, 19(9), 1498-1507.
13. Chen, Z., Zheng, Y., & Gee, J. C. (2023). Transmatch: A transformer-based multilevel dual-stream feature matching network for unsupervised deformable image registration. IEEE transactions on medical imaging, 43(1), 15-27.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/530-02-003