Lujie Li 1, Meng Wang1, Boyan Xu2, Xueying Zhao2, Tao Wu3, Zhi Dong1, Shiting Feng1
1Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, China
2MR Research, Beijing, China
3MR Research China, GE Healthcare, Beijing, China
Presenting Author: Lujie Li
Synopsis
Motivation:
Goals:
Approach:
Results:
Full abstract & presentation
The full text, figures, and any recorded presentation for this abstract are not shown here. Log in if you are a member or registered attendee with access.
Full abstracts, figures, and presentations for Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition are available to registered attendees. This content becomes freely available to the public roughly two years after the meeting.
To request or purchase access, contact the ISMRM Central Office at info@ismrm.org.
1. Committee, Q.S.M.C.O., et al., Recommended implementation of quantitative susceptibility mapping for clinical research in the brain: A consensus of the ISMRM electro-magnetic tissue properties study group. Magn Reson Med, 2024. 91(5): p. 1834-1862.
2. Dimov, A.V., et al., QSM Throughout the Body. J Magn Reson Imaging, 2023. 57(6): p. 1621-1640.
3. Li, J., et al., Quantitative susceptibility mapping (QSM) minimizes interference from cellular pathology in R2* estimation of liver iron concentration. J Magn Reson Imaging, 2018. 48(4): p. 1069-1079.
4. Lin, H., et al., Quantitative susceptibility mapping in combination with water-fat separation for simultaneous liver iron and fat fraction quantification. Eur Radiol, 2018. 28(8): p. 3494-3504.
5. Qu, Z., et al., Magnetic resonance quantitative susceptibility mapping in the evaluation of hepatic fibrosis in chronic liver disease: a feasibility study. Quant Imaging Med Surg, 2021. 11(4): p. 1170-1183.
6. Finnerty, E., et al., Noninvasive quantification of oxygen saturation in the portal and hepatic veins in healthy mice and those with colorectal liver metastases using QSM MRI. Magn Reson Med, 2019. 81(4): p. 2666-2675.
7. Meneses, J.P., et al., Liver PDFF estimation using a multi-decoder water-fat separation neural network with a reduced number of echoes. European Radiology, 2023. 33(9): p. 6557-6568.
8. Chan, K.S. and J.P. Marques, SEPIA-Susceptibility mapping pipeline tool for phase images. Neuroimage, 2021. 227: p. 117611.
9. Dong, J., et al., Simultaneous phase unwrapping and removal of chemical shift (SPURS) using graph cuts: application in quantitative susceptibility mapping. IEEE Trans Med Imaging, 2015. 34(2): p. 531-40.
10. Schofield, M.A. and Y. Zhu, Fast phase unwrapping algorithm for interferometric applications. Optics Letters, 2003. 28(14): p. 1194-1196.
11. Karsa, A. and K. Shmueli, SEGUE: A Speedy rEgion-Growing Algorithm for Unwrapping Estimated Phase. IEEE Transactions On Medical Imaging, 2018. 38(6): p. 1347-1357.
12. Dymerska, B., et al., Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO). Magnetic Resonance In Medicine, 2020. 85(4): p. 2294-2308.
13. Liu, T., et al., A novel background field removal method for MRI using projection onto dipole fields (PDF). NMR Biomed, 2011. 24(9): p. 1129-36.
14. Wei, H., et al., Streaking artifact reduction for quantitative susceptibility mapping of sources with large dynamic range. NMR Biomed, 2015. 28(10): p. 1294-303.