Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
552-02-003 / 552-02-003 ISMRM Abstract

Physics-Informed Synthetic MRI Data Enable Accurate and Generalisable Liver Fat Quantification Using Deep Learning

Primary: Acquisition & Reconstruction - AI methods
Secondary: Acquisition & Reconstruction - Quantitative Imaging: chest, abdomen, and pelvis
552-02-003 · Quantitative Imaging: Relaxometry, Multiparametric, MR Fingerprinting, and Synthetic MRI · Wednesday, 13 May, 1:40 PM–3:16 PM · Power Pitch Theatre 2
552-02-003 · Quantitative Imaging: Relaxometry, Multiparametric, MR Fingerprinting, and Synthetic MRI · Wednesday, 13 May, 1:40 PM–3:16 PM · Power Pitch Theatre 2
Keywords: Proton density fat fraction (PDFF) Generative AI Physics-informed Deep Learning Chemical Shift-Encoded MRI Water-fat separation
Accepted
Ting-Yu Lin1, Sergio Uribe 1, Zhaolin Chen2,3, Juan P Meneses1
1Department of Medical Imaging and Radiation Sciences, Monash University, Melbourne, Australia
2Monash Biomedical Imaging, Monash University, Clayton, Victoria, Clayton, Australia
3Department of Data Science and AI, Monash University, Clayton, Victoria, Clayton, Australia
Presenting Author: Sergio Uribe

Synopsis

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References

1. Eslam M, Newsome PN, Sarin SK, et al. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement. J Hepatol. 2020;73(1):202-209. doi:10.1016/j.jhep.2020.03.039 [doi]
2. Meneses JP, George Y, Hagemeyer C, Chen Z, Uribe S. A Physics-based Generative Model to Synthesize Training Datasets for MRI-based Fat Quantification. arXiv preprint arXiv:241208741. 2024.
3. Meneses JP, Qadir A, Surendran N, et al. Unbiased and reproducible liver MRI-PDFF estimation using a scan protocol-informed deep learning method. Eur Radiol. November 2024. doi:10.1007/s00330-024-11164-x [doi]
4. Jafari R, Spincemaille P, Zhang J, et al. Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training. Magn Reson Med. 2021. doi:10.1002/mrm.28546 [doi]
5. Meneses JP, Arrieta C, della Maggiora G, et al. Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes. Eur Radiol. April 2023. doi:10.1007/s00330-023-09576-2 [doi]
6. Hernando D, Kellman P, Haldar JP, Liang ZP. Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm. Magn Reson Med. 2010;63(1):79-90. doi:10.1002/mrm.22177 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/552-02-003