Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
402-03-005 ISMRM Abstract

PhoENIx: Assessing Robustness of the ISMRM 2024 MRSI Fitting Challenge Winner

Primary: Acquisition & Reconstruction - Spectroscopy and spectroscopic imaging
Secondary: Analysis Methods - Data Processing
402-03-005 · Metabolic Imaging and MR Spectroscopy: Methods and Application · Tuesday, 12 May, 1:40 PM–3:30 PM · Hall 1B
Keywords: Challenges 3D magnetic resonance spectroscopic imaging Spectral quantification Deep learning-based ISMRM 2024 MRSI Fitting Challenge
Accepted
Aaron P Osburg 1,2, Ekaterina Sazonova3, Wolfgang Bogner1,2,4, Amirmohammad Shamaei5, Bernhard Strasser1, Stanislav Motyka1,2,4
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 Engineering and Information Technology, Technical University of Vienna, Vienna, Austria
4Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
5Department of Electrical and Software Engineering, University of Calgary, Calgary, Canada
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. 2024 MRSI Data Processing and Quantitation Challenge (Accessed October 25, 2025).
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. 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]
8. van de Sande, D. M. J. et al. A review of machine learning applications for the proton MR spectroscopy workflow. Magnetic Resonance in Medicine vol. 90 1253–1270 Preprint at https://doi.org/10.1002/mrm.29793 (2023). [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/402-03-005