Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
452-03-001 / 452-03-001 ISMRM Abstract

A Universal Deep Model for Gibbs Artifacts Removal in MRI

Primary: Analysis Methods - Image Enhancement
Secondary: Acquisition & Reconstruction - Artifacts and Correction Strategies
452-03-001 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
452-03-001 · Next-Generation MRI Image Enhancement · Tuesday, 12 May, 4:00 PM–5:36 PM · Power Pitch Theatre 2
Keywords: Deep learning Gibbs artifacts
Accepted
Yu Zhou1, Jia Ning1, Hongyu Guo1, Wei Xi1, Hongbin Wang 2, Weinan Tang2
1Midea Corporate Research Center, Shanghai, China
2Beijing Wandong Medical Technology Co., Ltd, Beijing, China
Presenting Author: Hongbin Wang

Synopsis

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References

1. E. Kellner, B. Dhital, V. G. Kiselev, and M. Reisert, “Gibbs-ringing artifact removal based on local subvoxel-shifts,” Magnetic resonance in medicine, vol. 76, no. 5, pp. 1574–1581, 2016. DOI: 10.1002/mrm.26054 [doi]
2. H.H. Lee, D.S. Novikov, E. Fieremans, “Removal of partial Fourier‐induced Gibbs (RPG) ringing artifacts in MRI,” Magnetic resonance in medicine, vol. 86, no. 5, pp. 2733–2750, 2021.DOI: 10.1002/mrm.28830 [doi]
3. Y. Liu, E. Z. Chen, X. Chen, T. Chen, S. Sun, “An unsupervised framework for joint MRI super resolution and Gibbs artifact removal,” in Proceedings of the international conference on information processing in medical imaging, 2023, pp. 403-414.DOI: https://doi.org/10.1007/978-3-031-34048-2_31 [doi]
4. L. Dai, D. Wang, X. Mao, Z. Miao, L. Lu, Y. Ling, H. Tan, Z. Li, H. Guo, X. Liang, Q. Xu, Y. Li, “Development and evaluation of a deep learning model for multi-frequency Gibbs artifact elimination,” Quantitative imaging in medicine and surgery, vol. 15, no. 2, 2025, pp.1160-1174. PMID: 39995704 [pmid]
5. Y. Wang, Y. Song, H. Xie, W. Li, B. Hu, G. Yang, “Reduction of Gibbs artifacts in magnetic resonance imaging based on convolutional neural network,” in Proceedings of International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, 2017. DOI: 10.1109/CISP-BMEI.2017.8302197 [doi]
6. M.M. Jimeno, K.S. Ravi, Z. Jin, D. Oyekunle, G. Ogbole, S. Geethanath, “ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning,” Magnetic Resonance Imaging, vol. 89, pp. 42–48, 2022. DOI: 10.1016/j.mri.2022.02.002 [doi]
7. M. J. Muckley, B. Ades-Aron, A. Papaioannou, G. Lemberskiy, E. Solomon, Y. W. Lui, D. K. Sodickson, E. Fieremans, D. S. Novikov, F. Knoll,“Training a neural network for Gibbs and noise removal in diffusion MRI,” Magnetic resonance in medicine, vol. 85, no. 1, pp. 413–428, 2021. DOI: 10.1002/mrm.28395 [doi]
8. K. Zhang, W. Zuo, Y. Chen, D. Meng, L. Zhang, “Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising,” IEEE transactions on image processing,, vol. 26, no. 7, pp. 3142–3155 , 2017. DOI: 10.1109/TIP.2017.2662206 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/452-03-001