Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
560-06-005 ISMRM Abstract

SpectrumMAE- A Novel Adaptation of Masked Autoencoders for Super Resolved 1H-MRSI of Gliomas

Accepted
Abdullah Bas 1, Nate Tran2, Yan Li2, Janine M Lupo2, Esin Ozturk Isik1
1Institute of Biomedical Engineering, Bogazici University, Istanbul, Turkey
2Department of Radiology and Biomedical Imaging, University Of California, San Francisco (UCSF), United States of America
Presenting Author: Abdullah Bas

Synopsis

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References

1. Kupriyanova, Y., & Schrauwen-Hinderling, V. (2025). Advances in in vivo magnetic resonance spectroscopy for metabolic disorders. Frontiers in Endocrinology, 16. https://doi.org/10.3389/fendo.2025.1578333 [doi]
2. Brown, T. R., Kincaid, B. M., & Ugurbil, K. (1982). NMR chemical shift imaging in three dimensions. Proceedings of the National Academy of Sciences of the United States of America, 79(11), 3523–3526. https://doi.org/10.1073/pnas.79.11.3523 [doi]
3. Wilson, M., et al. (2019). Methodological consensus on clinical proton MRS of the brain: Review and recommendations. Magnetic Resonance in Medicine, 82(2), 527–550. https://doi.org/10.1002/mrm.27742 [doi]
4. Maudsley, A. A., et al. (2021). Advanced magnetic resonance spectroscopic neuroimaging: Experts' consensus recommendations. NMR in Biomedicine, 34(5), e4309. https://doi.org/10.1002/nbm.4309 [doi]
5. Banerjee, S., Ozturk-Isik, E., et al. (2006, August). Fast magnetic resonance spectroscopic imaging at 3 Tesla using autocalibrating parallel technique. In 2006 International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 1866-1869). IEEE. https://doi.org/10.1109/IEMBS.2006.260659 [doi]
6. Lam, F., Ma, C., Clifford, B., Johnson, C. L., & Liang, Z. P. (2016). High-resolution 1H-MRSI of the brain using SPICE: Data acquisition and image reconstruction. Magnetic Resonance in Medicine, 76(4), 1059–1070. https://doi.org/10.1002/mrm.26019 [doi]
7. Zierhut, M. L., Ozturk-Isik, E., Chen, A. P., Park, I., Vigneron, D. B., & Nelson, S. J. (2009). 1H spectroscopic imaging of human brain at 3 Tesla: comparison of fast three-dimensional magnetic resonance spectroscopic imaging techniques. Journal of Magnetic Resonance Imaging, 30(3), 473–480. https://doi.org/10.1002/jmri.21834 [doi]
8. Iqbal, Z., Nguyen, D., Hangel, G., Motyka, S., Bogner, W., & Jiang, S. (2019). Super-Resolution 1H Magnetic Resonance Spectroscopic Imaging Utilizing Deep Learning. Frontiers in Oncology, 9. https://doi.org/10.3389/fonc.2019.01010 [doi]
9. Iqbal, Z., Nguyen, D., Thomas, M. A., et al. (2021). Deep learning can accelerate and quantify simulated localized correlated spectroscopy. Scientific Reports, 11, 8727. https://doi.org/10.1038/s41598-021-88158-y [doi]
10. He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2021). Masked Autoencoders Are Scalable Vision Learners. arXiv. https://doi.org/10.48550/arXiv.2111.06377 [doi]
11. Li, Y. et al. (2013). Survival analysis in patients with newly diagnosed glioblastoma using pre-and postradiotherapy MR spectroscopic imaging. Neuro Oncol 15. https://doi.org/10.1093/neuonc/nos334 [doi]

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