Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
568-03-007 ISMRM Abstract

Clinical Evaluation of a Rapid Deep Learning Reconstructed Free-Breathing 2D CSE Imaging Sequence for Liver Quantification

Primary: Body - Liver
Secondary: Acquisition & Reconstruction - Quantitative Imaging: chest, abdomen, and pelvis
568-03-007 · MR Biomarkers of Hepatic Steatosis, Fibrosis, and Liver Disease · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row I
Keywords: Deep-learning-based image reconstruction Free-breathing quantitative liver MRI Chemical Shift-Encoded MRI Liver Iron Quantification
Accepted
Eugene Milshteyn 1, Nathan T Roberts2, Arnaud Guidon1, Rory L Cochran3, Mukesh G Harisinghani3, Amirkasra Mojtahed3
1GE HealthCare, San Ramon, United States of America
2GE HealthCare, Waukesha, United States of America
3Massachusetts General Hospital, Boston, United States of America
Presenting Author: Eugene Milshteyn

Synopsis

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References

1. Reeder SB, Yokoo T, França M, et al. Quantification of Liver Iron Overload with MRI: Review and Guidelines from the ESGAR and SAR. Radiology. 2023;307(1):e221856. doi:10.1148/radiol.221856 [doi]
2. Eskreis-Winkler S, Corrias G, Monti S, et al. IDEAL-IQ in an oncologic population: meeting the challenge of concomitant liver fat and liver iron. Cancer Imaging. 2018;18(1):51. doi:10.1186/s40644-018-0167-3 [doi]
3. Corrias G, Krebs S, Eskreis-Winkler S, et al. MRI liver fat quantification in an oncologic population: the added value of complex chemical shift-encoded MRI. Clin Imaging. 2018;52:193-199. doi:10.1016/j.clinimag.2018.08.002 [doi]
4. Reeder SB, Pineda AR, Wen Z, et al. Iterative decomposition of water and fat with echo asymmetry and least‐squares estimation (IDEAL): Application with fast spin‐echo imaging. Magn Reson Med. 2005;54(3):636-644. doi:10.1002/mrm.20624 [doi]
5. Starekova J, Zhao R, Colgan TJ, et al. Improved free-breathing liver fat and iron quantification using a 2D chemical shift–encoded MRI with flip angle modulation and motion-corrected averaging. Eur Radiol. 2022;32(8):5458-5467. doi:10.1007/s00330-022-08682-x [doi]
6. Zhao R, Zhang Y, Wang X, et al. Motion‐robust, high‐SNR liver fat quantification using a 2D sequential acquisition with a variable flip angle approach. Magn Reson Med. 2020;84(4):2004-2017. doi:10.1002/mrm.28263 [doi]
7. Tang, Jiayi, Tamada, Daiki, do Vale Souza, Raphael, et al. Optimization of Parallel Imaging in Flip Angle Modulated 2D Sequential CSE-MRI for SNR-Efficient and Motion-Robust Liver Fat Quantification. In: Proceedings of the 32nd Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM). Singapore; 2024:4627.
8. Milshteyn E, Guidon A, Tang J, et al. Single-Site Experience of Liver Fat and Iron Quantitation with a 2D Flip Angle Modulated CSE-MRI Sequence in the Clinical Setting. In: Proceedings of the 33rd Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM). Honolulu, Hawaii, USA; 2025:2834. doi:10.58530/2025/2834 [doi]
9. Lebel RM. Performance characterization of a novel deep learning-based MR image reconstruction pipeline. August 2020. doi:10.48550/arXiv.2008.06559 [doi]
10. Pradham P, Younan NH, King RL. 16 - Concepts of image fusion in remote sensing applications. In: Stathaki T, ed. Image Fusion. Oxford: Academic Press; 2008:393-428. doi:10.1016/B978-0-12-372529-5.00019-6 [doi]

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/568-03-007