Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
570-03-182 ISMRM Abstract

Deep Learning Super-Resolution for T1-Weighted Magnetic Resonance Imaging at 0.5T

Primary: Acquisition & Reconstruction - AI methods
Secondary: Acquisition & Reconstruction - Image Reconstruction: AI
570-03-182 · AI Methods · Wednesday, 13 May, 9:15 AM–10:10 AM · Traditional Posters | Exhibition Hall
Keywords: Brain Super-resolution Deep learning 0.5T T1-weighted Imaging
Accepted
Ying Yang 1, Diego Martinez1,2, Amgad Louka2, Alexander J Mertens1, Ian Connell1,2,3
1Department of Medical Biophysics, University of Toronto, Toronto, Canada
2Biomedical Engineering, University Health Network, Toronto, Canada
3CenteR for Advancing Neurotechnical Innovation to Applications, University Health Network, Toronto, Canada
Presenting Author: Ying Yang

Synopsis

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References

1. Arnold TC, Freeman CW, Litt B, Stein, JM. Low-field MRI: Clinical promise and challenges. J Magn Reson Imaging. 2023;57(1):25-44. doi: 10.1002/jmri.28408 [doi]
2. Xu W, Jia S, Cui ZX, Zhu Q, Liu X, Liang D, et al. Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging. Bioengineering (Basel). 2023;10(9):1107. doi: 10.3390/bioengineering10091107 [doi]
3. de Leeuw den Bouter ML, Ippolito G, O’Reilly TPA, Remis RF, van Gijzen MB, Webb AG. Deep learning-based single image super-resolution for low-field MR brain images. Sci Rep. 2022;12:6362. doi: 10.1038/s41598-022-10298-6 [doi]
4. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely Connected Convolutional Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017 Jul 21-26; Honolulu, HI, USA. Piscataway (NJ): IEEE; 2017. p. 2261-2269. doi: 10.1109/CVPR.2017.243 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/570-03-182