569-03-010 ISMRM Abstract

CONSIS-Net — Consistency-based Iterative Super-Resolution unrolled Network for low-field knee MRI

Primary: Physics & Engineering - Low-Field MRI

Secondary: Musculoskeletal - Whole Joint

569-03-010 · Super-Resolving MRI: Methods and Applications · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row J

Keywords: Low-Field MRI Super-resolution Deep learning Knee imaging

Accepted

Sebástian A Ibarra ¹, Steren Chabert^1,2,3, Ronal Coronado^1,4,5, Claudia Prieto^1,6,7

¹Millennium Institute for Intelligent Healthcare Engineering - iHEALTH, Santiago, Chile

²Biomedical Engineering School, Faculty of Engineering, Universidad de Valparaíso, Valparaíso, Chile

³Center of Interdisciplinary Biomedical and Engineering Research for Health - MEDING, Universidad de Valparaíso, Valparaíso, Chile

⁴Núcleo de Investigación en Data Science (NIDS), Facultad de Ingeniería y Negocios, Universidad de Las Americas, Santiago, Chile

⁵Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile

⁶School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

⁷School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile, Chile

Presenting Author: Sebástian A Ibarra

Synopsis

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References

1. T. C. Arnold, C. W. Freeman, B. Litt, and J. M. Stein, “Low-field mri: Clinical promise221 and challenges,” Journal of Magnetic Resonance Imaging, vol. 57, no. 1, 2022. doi: https:222//doi.org/10.1002/jmri.28408. [Online]. Available: https://onlinelibrary.wiley.com/223doi/full/10.1002/jmri.28408. [doi]

2. D. Dutta, D. Chetia, N. Sonowal, and S. K. Kalita, State-of-the-art transformer models for image super-resolution: Techniques, challenges, and applications, 2025. [Online]. Available:https://arxiv.org/abs/2501.07855.

3. Z. Ji et al., “Deep learning-based magnetic resonance image super-resolution: A survey,” Neural Computing and Applications, vol. 36, 2024. doi: https://doi.org/10.1007/s00521-024-09890-w. [doi]

4. J. Kim, J. Nang, and J. Choe, “LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-Resolution,” arXiv e-prints, arXiv:2409.03516, arXiv:2409.03516, Sep. 2024. doi: 10.48550/arXiv.2409.03516. arXiv: 2409.03516 [cs.CV]. [doi]

5. J. Zbontar et al., Fastmri: An open dataset and benchmarks for accelerated mri, 2019. arXiv:1811.08839 [cs.CV]. [Online]. Available: https://arxiv.org/abs/1811.08839.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/569-03-010