Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
364-04-007 ISMRM Abstract

Improved Reconstruction of MR Phase-varied Images Using Real-value Trained Score-based Diffusion Model

Primary: Acquisition & Reconstruction - Image Reconstruction: AI
Secondary: Acquisition & Reconstruction - Subsampling: Parallel imaging, compressed sensing and low-rank modeling
364-04-007 · Image Reconstruction Using Generative Models · Monday, 11 May, 2:45 PM–3:40 PM · Digital Posters Row E
Keywords: AI/ML Image Reconstruction Diffusion Modeling Image Reconstruction Complex image
Accepted
Satoshi ITO 1, Tomoki Sawai2
1Graduate School of Regional Development and Creativity, Utsunomiya University, Utsunomiya, Japan
2Utsunomiya University, Utsunomiya, Japan
Presenting Author: Satoshi ITO

Synopsis

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References

1. J. Song, C. Meng, S. Ermon, “Denoising diffusion implicit models,” arXiv preprint arXiv:2010.02502, 2020.
2. H. Chung, J. C. Ye, “Score-based diffusion models for accelerated MRI,” Medical Image Analysis vol.80, 102479, 2022. doi: 10.1016/j.media.2022.102479 [doi]
3. S. Ito, K. Nakamura, Y. Yamada, “An Efficient Compressed Sensing Reconstruction Robust to Phase Variation on MR Images,” International Society of Magnetic Resonance in Medicine 21th Scientific Meeting, Saltlake, USA, 2604, 2013.
4. S. Ouchi, S Ito, “Efficient complex-valued image reconstruction for compressed sensing MRI using single real-valued convolutional neural network,” Magnetic Resonance Imaging, vol. 101, pp. 13-24, 2023. doi: 10.1016/j.mri.2023.03.011 [doi]
5. O. Ronneberger, P. Fischer, T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” MICCAI 2015. Lecture Notes in Computer Science, vol. 9351. Springer, Cham. doi: 10.1007/978-3-319-24574-4_28 [doi]
6. CM. Fan, TJ Liu, KH. Liu, “SUNet: Swin Transformer UNet for Image Denoising,” 2022 IEEE International Symposium on Circuits and Systems (ISCAS), Austin, TX, USA, 2022, pp. 2333-2337. doi: 10.1109/ISCAS48785.2022.9937486. [doi]
7. Y. Yang, J. Sun, H. Li, Z. Xu, “ADMM-CSNet: a deep learning approach for image compressive sensing,” IEEE Trans Pattern Anal Mach Intell, Vol.42, pp.521–38. 2020. doi: 10.1109/TPAMI.2018.2883941 [doi]
8. M.W. Matthès, Y. Bromberg, S.M. Popoff, “Learning and avoiding disorder in multimode fibers,” Physical Review X Vol.11, 021060, 2021. doi: 10.1103/PhysRevX.11.021060. [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/364-04-007