Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
366-05-008 ISMRM Abstract

Variable Flip Angle T1 mapping of the liver at 0.55T

Primary: Acquisition & Reconstruction - Quantitative Imaging: chest, abdomen, and pelvis
Secondary: Body - Liver
366-05-008 · Quantitative Applications in Body MRI: Make the Numbers Count · Monday, 11 May, 4:10 PM–5:05 PM · Digital Posters Row G
Keywords: Body Imaging 0.55T Variable Flip Angles B1 inhomogeneity Liver T1
Accepted
Majd Helo 1,2, Marcel D Nickel2, Thomas Küstner1
1Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospital of Tuebingen, Tuebingen, Germany
2Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany
Presenting Author: Majd Helo

Synopsis

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References

1. Fellner, C., et al., Water-Fat Separated T1 Mapping in the Liver and Correlation to Hepatic Fat Fraction. Diagnostics (Basel), 2023. 13(2).
2. Roberts, N.T., et al., Confounder-corrected T(1) mapping in the liver through simultaneous estimation of T(1) , PDFF, R2* , and B1+ in a single breath-hold acquisition. Magn Reson Med, 2023. 89(6): p. 2186-2203.
3. Preibisch, C. and R. Deichmann, Influence of RF spoiling on the stability and accuracy of T1 mapping based on spoiled FLASH with varying flip angles. Magn Reson Med, 2009. 61(1): p. 125-35.
4. Baudrexel, S., et al., T(1) mapping with the variable flip angle technique: A simple correction for insufficient spoiling of transverse magnetization. Magn Reson Med, 2018. 79(6): p. 3082-3092.
5. Keenan, K.E., et al., T1 and T2 measurements across multiple 0.55T MRI systems using open-source vendor-neutral sequences. Magn Reson Med, 2025. 93(1): p. 289-300.
6. Campbell-Washburn, A.E., et al., Opportunities in interventional and diagnostic imaging by using high-performance low-field-strength MRI. Radiology, 2019. 293(2): p. 384-393.
7. Chung, S., et al., Rapid B1+ mapping using a preconditioning RF pulse with TurboFLASH readout. Magnetic resonance in medicine, 2010. 64(2): p. 439-446.
8. Hammernik, K., et al., Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination. Magnetic Resonance in Medicine, 2021. 86(4): p. 1859-1872.
9. Helo, M., et al., Liver fat quantification at 0.55 T enabled by locally low-rank enforced deep learning reconstruction. Magn Reson Med, 2025.
10. Huang, J., et al., Evaluation on the generalization of a learned convolutional neural network for MRI reconstruction. Magnetic resonance imaging, 2022. 87: p. 38-46.
11. Wei, H., et al., Enhancing gadoxetic acid-enhanced liver MRI: a synergistic approach with deep learning CAIPIRINHA-VIBE and optimized fat suppression techniques. Eur Radiol, 2024. 34(10): p. 6712-6725.
12. Graf, R., et al., VIBESegmentator: full body MRI segmentation for the NAKO and UK Biobank. Eur Radiol, 2025.
13. Fuderer, M., et al., Color‐map recommendation for MR relaxometry maps. Magnetic Resonance in Medicine, 2025. 93(2): p. 490-506.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/366-05-008