Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
601-03-009 ISMRM Abstract

Development and Validation of Self-Supervised Deep Learning-Based Spectral-Temporal Fitting of Dynamic 2H-MRSI Data at 7T

Accepted
Aaron P Osburg 1,2, Amirmohammad Shamaei3, Bernhard Strasser1, Fabian Niess1, Anna Duguid1, Viola Bader1, Sabina Frese1, Lukas Hingerl1, Hauke Fischer1, Ivan Petrovic1, William T Clarke4, Georg Langs2,5, Wolfgang Bogner1,2,6, Stanislav Motyka1,2,6
1High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
2Comprehensive Centre for AI in Medicine (CAIM), Medical University of Vienna, Vienna, Austria
3Department of Electrical and Software Engineering, University of Calgary, Calgary, Canada
4Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
5Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
6Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
Presenting Author: Aaron P Osburg

Synopsis

Motivation:
Goals:
Approach:
Results:
Full abstract & presentation

The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.

Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.

To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.

Log in

References

1. Manji, H. et al. Impaired mitochondrial function in psychiatric disorders. Nature Reviews Neuroscience vol. 13 293–307 Preprint at https://doi.org/10.1038/nrn3229 (2012). [doi]
2. Norat, P. et al. Mitochondrial dysfunction in neurological disorders: Exploring mitochondrial transplantation. npj Regenerative Medicine vol. 5 Preprint at https://doi.org/10.1038/s41536-020-00107-x (2020). [doi]
3. Koppenol, W. H., Bounds, P. L. & Dang, C. V. Otto Warburg’s contributions to current concepts of cancer metabolism. Nature Reviews Cancer vol. 11 325–337 Preprint at https://doi.org/10.1038/nrc3038 (2011). [doi]
4. Niess, F. et al. Whole-brain deuterium metabolic imaging via concentric ring trajectory readout enables assessment of regional variations in neuronal glucose metabolism. Hum Brain Mapp 45, (2024) doi:10.1002/hbm.26686. [doi]
5. Li, X. et al. Quantitative mapping of key glucose metabolic rates in the human brain using dynamic deuterium magnetic resonance spectroscopic imaging. PNAS Nexus 4, (2025). https://doi.org/10.1093/pnasnexus/pgaf072 [doi]
6. Lu, M., Zhu, X. H., Zhang, Y., Mateescu, G. & Chen, W. Quantitative assessment of brain glucose metabolic rates using in vivo deuterium magnetic resonance spectroscopy. Journal of Cerebral Blood Flow and Metabolism 37, 3518–3530 (2017) doi:10.1177/0271678X17706444. [doi]
7. De Feyter, H. M. & de Graaf, R. A. Deuterium metabolic imaging – Back to the future. Journal of Magnetic Resonance vol. 326 Preprint at https://doi.org/10.1016/j.jmr.2021.106932 (2021). [doi]
8. Provencher, S. W. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med 30, 672–679 (1993) doi:10.1002/mrm.1910300604.. [doi]
9. Shamaei, A., Starcukova, J. & Starcuk, Z. Physics-informed deep learning approach to quantification of human brain metabolites from magnetic resonance spectroscopy data. Comput Biol Med 158, (2023). https://doi.org/10.1016/j.compbiomed.2023.106837 [doi]
10. Shamaei, A., Motyka, S. & Wilhem, W. PHIVE: Physics-Informed Variational Encoder for Brain Metabolite Mapping at 7T. medRxiv 2025.01.02.25319930 (2025) doi:10.1101/2025.01.02.25319930. [doi]
11. Gurbani, S. S., Sheriff, S., Maudsley, A. A., Shim, H. & Cooper, L. A. D. Incorporation of a spectral model in a convolutional neural network for accelerated spectral fitting. Magn Reson Med 81, 3346–3357 (2019) doi:10.1002/mrm.27641. [doi]
12. Turco, F., Capiglioni, M., Weng, G. & Slotboom, J. TensorFit: A torch-based tool for ultrafast metabolite fitting of large MRSI data sets. Magn Reson Med 92, 447–458 (2024). https://doi.org/10.1002/mrm.30084 [doi]
13. Giuffrida, A. S. et al. NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High-Resolution Short-Echo-Time MR Spectroscopy Datasets. Radiol Artif Intell 7, (2025). https://doi.org/10.3390/cancers17030423 [doi]
14. Clarke, W. T., Ligneul, C., Cottaar, M., Ip, I. B. & Jbabdi, S. Universal dynamic fitting of magnetic resonance spectroscopy. Magn Reson Med 91, 2229–2246 (2024) doi:10.1002/mrm.30001. [doi]
15. Tal, A. The future is 2D: spectral-temporal fitting of dynamic MRS data provides exponential gains in precision over conventional approaches. Magnetic Resonance in Medicine vol. 89 499–507 Preprint at https://doi.org/10.1002/mrm.29456 (2023). [doi]
16. Osburg, A. P. et al. Physics-Informed Deep Autoencoder for Dynamic Fitting of Deuterium Metabolic Imaging Data [abstract]. 2025 ISMRM & ISMRT Annual Meeting & Exhibition, Honolulu. (2025).
17. Osburg, A. P. et al. A Deep Autoencoder for Fast Spectral-Temporal Fitting of Dynamic Deuterium Metabolic Imaging Data at 7T. medRxiv 2025.09.09.25335269 (2025) doi:10.1101/2025.09.09.25335269. [doi]
18. Hingerl, L. et al. Clinical High-Resolution 3D-MR Spectroscopic Imaging of the Human Brain at 7 T. Invest Radiol 55, 239–248 (2020) doi:10.1097/RLI.0000000000000626. [doi]
19. Duguid, A. et al. Comparison of Low-Rank Denoising Methods for Dynamic Deuterium MRSI at 7 T. NMR Biomed 38, (2025) doi:10.1002/nbm.70125. [doi]

Cite this abstract