Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
569-04-007 ISMRM Abstract

Enhancing Reliability in Deep Learning Metabolite Quantification: Evaluating Uncertainty Estimation Approaches

Primary: Acquisition & Reconstruction - Spectroscopy and spectroscopic imaging
Secondary: Acquisition & Reconstruction - AI methods
569-04-007 · Spectroscopy: Acquisition and Reconstruction · Wednesday, 13 May, 2:35 PM–3:30 PM · Digital Posters Row J
Keywords: Deep learning Magnetic resonance spectroscopic imaging Uncertainty estimation Metabolite Quantification Bayesian Neural Networks
Accepted
Sara Zatezalo1,2,3, Ludovica Romanin1, Sébastien Courvoisier4, Gian Franco Piredda1, Tom Hilbert1,2,3, Thomas Yu1,2,3, Antoine Klauser 1
1Swiss Innovation Hub, Siemens Healthineers International AG, Lausanne, Switzerland
2LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
3Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
4Center for Biomedical Imaging (CIBM), Lausanne, Switzerland
Presenting Author: Antoine Klauser

Synopsis

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References

1. [1] Merkofer, J. P., Amirrajab, S., Veta, M., Versluis, M. J., A. Jansen, J. F., & Breeuwer, M. (2023). A review of machine learning applications for the proton MR spectroscopy workflow. Magnetic Resonance in Medicine, 90(4), 1253-1270. https://doi.org/10.1002/mrm.29793 [doi]
2. [2] Rizzo, R., Dziadosz, M., Kyathanahally, S. P., Shamaei, A., & Kreis, R. (2023). Quantification of MR spectra by deep learning in an idealized setting: Investigation of forms of input, network architectures, optimization by ensembles of networks, and training bias. Magnetic Resonance in Medicine, 89(5), 1707-1727. https://doi.org/10.1002/mrm.29561 [doi]
3. [3] S Courvoisier, A Klauser, P Lichard, M Kocher, F Lazeyras, (2019) High-Resolution Magnetic Resonance Spectroscopic Imaging quantification by Convolutional Neural Network, Proc. Intl. Soc. Mag. Reson. Med. 27, #0519
4. [4] Antoine Klauser, Bernhard Strasser, Wolfgang Bogner, Lukas Hingerl, Sebastien Courvoisier, Claudiu Schirda, Bruce R. Rosen, Francois Lazeyras, Ovidiu C. Andronesi; ECCENTRIC: A fast and unrestrained approach for high-resolution in vivo metabolic imaging at ultra-high field MR. Imaging Neuroscience 2024; 2 imag–2–00313. doi: https://doi.org/10.1162/imag_a_00313 [doi]
5. [5] T. Diskin, Y. C. Eldar and A. Wiesel, "Learning to Estimate Without Bias," in IEEE Transactions on Signal Processing, vol. 71, pp. 2162-2171, 2023, doi: 10.1109/TSP.2023.3284372. [doi]
6. [6] Ranganath Krishnan, Pi Esposito, & Mahesh Subedar. (2022). Bayesian-Torch: Bayesian neural network layers for uncertainty estimation (v0.2.0). Zenodo. https://doi.org/10.5281/zenodo.5908307 [doi]
7. [7] Levi D, Gispan L, Giladi N, Fetaya E. Evaluating and Calibrating Uncertainty Prediction in Regression Tasks. Sensors (Basel). 2022 Jul 25;22(15):5540. doi: 10.3390/s22155540. PMID: 35898047; PMCID: PMC9330317. [doi] [pmid]
8. [8] Youngseog Chung, Ian Char, Han Guo, Jeff Schneider and Willie Neiswanger. (2021). "Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification", https://arxiv.org/abs/2109.10254

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/569-04-007