Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
401-03-002 ISMRM Abstract

Inline reconstruction of High-Resolution 1H MRSI Using Non-Cartesian Trajectories at Ultra-High Field for direct clinical use

Primary: Acquisition & Reconstruction - Spectroscopy and spectroscopic imaging
Secondary: Analysis Methods - Software Tools
401-03-002 · End-to-End Software Innovation · Tuesday, 12 May, 1:40 PM–3:30 PM · Hall 1A
Keywords: Brain AI/ML Image Reconstruction MRI Workflow optimzation 3D magnetic resonance spectroscopic imaging Metabolic imaging
Accepted
Ludovica Romanin1, Sara Zatezalo1,2,3, Kelvin Chow4,5, Lumeng Cui4, Thomas Yu1,2,3, Gian Franco Piredda1, Tom Hilbert1,2,3, Ovidiu C Andronesi6, Paul Weiser6, Antoine Klauser 1
1Swiss Innovation Hub, Siemens Healthineers International AG, Lausanne, Switzerland
2Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
3LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
4Siemens Healthcare Limited, Montreal, Canada
5Siemens Medical Solutions USA, Inc., Malvern, United States of America
6Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, United States of America
Presenting Author: Antoine Klauser

Synopsis

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References

1. Klauser A, Strasser B, Bogner W, et al. 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:10.1162/imag_a_00313 [doi]
2. Weiser PJ, Langs G, Motyka S, et al. WALINET: A water and lipid identification convolutional neural network for nuisance signal removal in 1H MR spectroscopic imaging. Magnetic Resonance in Medicine. 2025;93(4):1430-1442. doi:10.1002/mrm.30402 [doi]
3. Weiser PJ, Langs G, Bogner W, et al. Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging. NeuroImage. 2025;309:121045. doi:10.1016/j.neuroimage.2025.121045 [doi]
4. Chow K, Wallace T, Fyrdahl A, et al. Standardization of Containerized “MRD Apps” for Reproducible and Deployable Research. In: Proc. Intl. Soc. Mag. Reson. Med. 31. 2023. doi:https://doi.org/10.58530/2023/4633 [doi]
5. Truong P, Chow K, Cui L, et al. Automatic, Online Reconstruction of Advanced Single Voxel and Spectroscopic Imaging Sequences. In: Proc. Intl. Soc. Mag. Reson. Med. 33. 2025. doi:https://doi.org/10.58530/2025/0595 [doi]
6. Uecker M, Lai P, Murphy MJ, et al. ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA. Magnetic Resonance in Medicine. 2014;71(3):990-1001. doi:10.1002/mrm.24751 [doi]
7. Chow K. Example client/server for streaming ISMRM Raw Data protocol. Accessed October 27, 2025. https://github.com/kspaceKelvin/python-ismrmrd-server
8. Inati SJ, Naegele JD, Zwart NR, et al. ISMRM Raw data format: A proposed standard for MRI raw datasets. Magnetic Resonance in Medicine. 2017;77(1):411-421. doi:10.1002/mrm.26089 [doi]
9. Courvoisier S, Klauser A, Lichard P, Kocher M, Lazeyras F. High-Resolution Magnetic Resonance Spectroscopic Imaging quantification by Convolutional Neural Network. In: Proc. Intl. Soc. Mag. Reson. Med. 27. 2019.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/401-03-002