Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
403-02-006 ISMRM Abstract

Graph-Attention Fusion with Retrieval Prompt Learning for Brain MRI Synthesis

Accepted
Ning Jiang1,2, Zhengyong Huang1,2, 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

1. Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Milham, M. P. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.
2. Duffy, B. A., Zhao, L., Sepehrband, F., Min, J., Wang, D. J., Shi, Y., ... & Alzheimer's Disease Neuroimaging Initiative. (2021). Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions. Neuroimage, 230, 117756.
3. Chen, W., Wu, S., Wang, S., Li, Z., Yang, J., Yao, H., ... & Song, X. (2025). Multi-contrast image super-resolution with deformable attention and neighborhood-based feature aggregation (DANCE): Applications in anatomic and metabolic MRI. Medical Image Analysis, 99, 103359.
4. Yang, L., He, Z., Zhong, T., Li, C., Zhu, D., Han, J., ... & Zhang, T. (2024, October). Brain Cortical Functional Gradients Predict Cortical Folding Patterns via Attention Mesh Convolution. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 140-149). Cham: Springer Nature Switzerland.
5. Masouleh, S. K., Plachti, A., Hoffstaedter, F., Eickhoff, S., & Genon, S. (2020). Characterizing the gradients of structural covariance in the human hippocampus. Neuroimage, 218, 116972.
6. Tian, Y., Chen, H., Xu, C., & Wang, Y. (2024). Image processing GNN: Breaking rigidity in super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 24108-24117).
7. Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., & Yang, M. H. (2022). Restormer: Efficient transformer for high-resolution image restoration. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 5728-5739).
8. Nárai, Á., Hermann, P., Auer, T., Kemenczky, P., Szalma, J., Homolya, I., ... & Vidnyánszky, Z. (2022). Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans. Scientific data, 9(1), 630.
9. http://brain-development.org/ixi-dataset/.
10. Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., ... & Van Leemput, K. (2014). The multimodal brain tumor image segmentation benchmark (BRATS). IEEE transactions on medical imaging, 34(10), 1993-2024.
11. Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., ... & Shi, W. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681-4690).
12. Dalmaz, O., Yurt, M., & Çukur, T. (2022). ResViT: Residual vision transformers for multimodal medical image synthesis. IEEE Transactions on Medical Imaging, 41(10), 2598-2614.
13. Jang, S. I., Pan, T., Li, Y., Heidari, P., Chen, J., Li, Q., & Gong, K. (2023). Spach Transformer: Spatial and channel-wise transformer based on local and global self-attentions for PET image denoising. IEEE transactions on medical imaging, 43(6), 2036-2049.
14. Yang, Z., Chen, H., Qian, Z., Yi, Y., Zhang, H., Zhao, D., ... & Xu, Y. (2024, October). All-in-one medical image restoration via task-adaptive routing. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 67-77). Cham: Springer Nature Switzerland.

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