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Assessment of MRF for simultaneous T1 and T2 quantification and water–fat separation in the liver at 0.55 T

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Abstract

Objective

The goal of this work was to assess the feasibility of performing MRF in the liver on a 0.55 T scanner and to examine the feasibility of water–fat separation using rosette MRF at 0.55 T.

Materials and methods

Spiral and rosette MRF sequences were implemented on a commercial 0.55 T scanner. The accuracy of both sequences in T1 and T2 quantification was validated in the ISMRM/NIST system phantom. The efficacy of rosette MRF in water-fat separation was evaluated in simulations and water/oil phantoms. Both spiral and rosette MRF were performed in the liver of healthy subjects.

Results

In the ISMRM/NIST phantom, both spiral and rosette MRF achieved good agreement with reference values in T1 and T2 measurements. In addition, rosette MRF enables water–fat separation and can generate water- and fat- specific T1 maps, T2 maps, and proton density images from the same dataset for a spatial resolution of 1.56 × 1.56 × 5mm3 within the acquisition time of 15 s.

Conclusion

It is feasible to measure T1 and T2 simultaneously in the liver using MRF on a 0.55 T system with lower performance gradients compared to state-of-the-art 1.5 T and 3 T systems within an acquisition time of 15 s. In addition, rosette MRF enables water–fat separation along with T1 and T2 quantification with no time penalty.

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Data availability

All data presensted in this study are available upon request.

References

  1. Ma D, Gulani V, Seiberlich N, Liu K, Sunshine JL, Duerk JL, Griswold MA (2013) Magnetic resonance fingerprinting. Nature 495:187–192

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Jiang Y, Ma D, Seiberlich N, Gulani V, Griswold M (2015) MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout. Magn Reson Med 74:1621–1631

    Article  CAS  PubMed  Google Scholar 

  3. Hamilton JI, Jiang Y, Chen Y, Ma D, Lo WC, Griswold M, Seiberlich N (2017) MR fingerprinting for rapid quantification of myocardial T1, T2, and proton spin density. Magn Reson Med 77:1446–1458

    Article  PubMed  Google Scholar 

  4. Jaubert O, Cruz G, Bustin A, Schneider T, Lavin B, Koken P, Hajhosseiny R, Doneva M, Rueckert D, Botnar RM, Prieto C (2019) Water–fat Dixon cardiac magnetic resonance fingerprinting. Magn Reson Med 00:1–17

    CAS  Google Scholar 

  5. Chen Y, Jiang Y, Pahwa S, Ma D, Lu L, Twieg MD, Wright KL, Seiberlich N, Griswold MA, Gulani V (2016) MR fingerprinting for rapid quantitative abdominal imaging. Radiology 279:278–286

    Article  PubMed  Google Scholar 

  6. Jaubert O, Arrieta C, Cruz G, Bustin A, Schneider T, Georgiopoulos G, Masci PG, Sing-Long C, Botnar RM, Prieto C (2020) Multi-parametric liver tissue characterization using MR fingerprinting: simultaneous T1, T2, T2*, and fat fraction mapping. Magn Reson Med. https://doi.org/10.1002/mrm.28311

    Article  PubMed  Google Scholar 

  7. Cao P, Wang Z, Liu C, Li T, Hui ES, Cai J (2022) Motion-resolved and free-breathing liver MRF. Magn Reson Imaging 91:69–80

    Article  PubMed  Google Scholar 

  8. van Riel MHC, Yu Z, Hodono S, Xia D, Chandarana H, Fujimoto K, Cloos MA (2021) Free-breathing abdominal T1 mapping using an optimized MR fingerprinting sequence. NMR Biomed. https://doi.org/10.1002/NBM.4531

    Article  PubMed  PubMed Central  Google Scholar 

  9. Chen Y, Panda A, Pahwa S, Hamilton JI, Dastmalchian S, McGivney DF, Ma D, Batesole J, Seiberlich N, Griswold MA, Plecha D, Gulani V (2019) Three-dimensional MR fingerprinting for quantitative breast imaging. Radiology 290:33–40

    Article  PubMed  Google Scholar 

  10. Panda A, Obmann VC, Lo WC, Margevicius S, Jiang Y, Schluchter M, Patel IJ, Nakamoto D, Badve C, Griswold MA, Jaeger I, Ponsky LE, Gulani V (2019) MR fingerprinting and ADC mapping for characterization of lesions in the transition zone of the prostate gland. Radiology 292:685–694

    Article  PubMed  Google Scholar 

  11. Yu AC, Badve C, Ponsky LE, Pahwa S, Dastmalchian S, Rogers M, Jiang Y, Margevicius S, Schluchter M, Tabayoyong W, Abouassaly R, McGivney D, Griswold MA, Gulani V (2017) Development of a combined MR fingerprinting and diffusion examination for prostate cancer. Radiology 283:729–738

    Article  PubMed  Google Scholar 

  12. Campbell-Washburn AE, Ramasawmy R, Restivo MC, Bhattacharya I, Basar B, Herzka DA, Hansen MS, Rogers T, Patricia Bandettini W, McGuirt DR, Mancini C, Grodzki D, Schneider R, Majeed W, Bhat H, Xue H, Moss J, Malayeri AA, Jones EC, Koretsky AP, Kellman P, Chen MY, Lederman RJ, Balaban RS (2019) Opportunities in interventional and diagnostic imaging by using high-performance low-field-strength MRI. Radiology 293:384–393

    Article  PubMed  Google Scholar 

  13. Campbell-Washburn AE, Jiang Y, Körzdörfer G, Nittka M, Griswold MA (2021) Feasibility of MR fingerprinting using a high-performance 0.55 T MRI system. Magn Reson Imaging 81:88–93

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Mickevicius NJ, Kim JP, Zhao J, Morris ZS, Hurst NJ, Glide-Hurst CK (2021) Toward magnetic resonance fingerprinting for low-field MR-guided radiation therapy. Med Phy. https://doi.org/10.1002/mp.15202

    Article  Google Scholar 

  15. Sarracanie M (2021) Fast quantitative low-field magnetic resonance imaging with OPTIMUM-optimized magnetic resonance fingerprinting using a stationary steady-state cartesian approach and accelerated acquisition schedules. Invest Radiol. https://doi.org/10.1097/RLI.0000000000000836

    Article  PubMed Central  Google Scholar 

  16. Liu Y, Hamilton J, Eck B, Griswold M, Seiberlich N (2021) Myocardial T1 and T2 quantification and water–fat separation using cardiac MR fingerprinting with rosette trajectories at 3T and 1.5T. Magn Reson Med 85:103–119

    Article  CAS  PubMed  Google Scholar 

  17. Noll DC (1997) Multishot rosette trajectories for spectrally selective MR imaging. IEEE Trans Med Imaging 16:372–377

    Article  CAS  PubMed  Google Scholar 

  18. Hamilton JI, Jiang Y, Ma D, Lo W-C, Gulani V, Griswold M, Seiberlich N (2018) Investigating and reducing the effects of confounding factors for robust T1 and T2 mapping with cardiac MR fingerprinting. Magn Reson Imaging 53:40–51

    Article  PubMed  PubMed Central  Google Scholar 

  19. McGivney DF, Pierre E, Ma D, Jiang Y, Saybasili H, Gulani V, Griswold MA (2014) SVD compression for magnetic resonance fingerprinting in the time domain. IEEE Trans Med Imaging 33:2311–2322

    Article  PubMed  PubMed Central  Google Scholar 

  20. Fessler JA (2007) On NUFFT-based gridding for non-Cartesian MRI. J Magn Reson 188:191–195

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Duyn JH, Yang Y, Frank JA, van der Veen JW (1998) Simple correction method for k-space trajectory deviations in MRI. J Magn Reson 132:150–3

    Article  CAS  PubMed  Google Scholar 

  22. Hamilton JI, Jiang Y, Ma D, Chen Y, Lo W-C, Griswold M, Seiberlich N (2018) Simultaneous multislice cardiac magnetic resonance fingerprinting using low rank reconstruction. NMR Biomed 32:e4041

    PubMed  PubMed Central  Google Scholar 

  23. Assländer J, Cloos MA, Knoll F, Sodickson DK, Hennig J, Lattanzi R (2018) Low rank alternating direction method of multipliers reconstruction for MR fingerprinting. Magn Reson Med 79:83–96

    Article  PubMed  Google Scholar 

  24. Zhang T, Pauly JM, Levesque IR (2015) Accelerating parameter mapping with a locally low rank constraint. Magn Reson Med 73:655–661

    Article  PubMed  Google Scholar 

  25. Lima da Cruz G, Bustin A, Jaubert O, Schneider T, Botnar RM, Prieto C (2019) Sparsity and locally low rank regularization for MR fingerprinting. Magn Reson Med 81:3530–3543

    PubMed  PubMed Central  Google Scholar 

  26. Yu H, Shimakawa A, McKenzie CA, Brodsky E, Brittain JH, Reeder SB (2008) Multiecho water-fat separation and simultaneous R*2 estimation with multifrequency fat spectrum modeling. Magn Reson Med 60:1122–1134

    Article  PubMed  PubMed Central  Google Scholar 

  27. Tsao J, Jiang Y (2013) Hierarchical IDEAL: Fast, robust, and multiresolution separation of multiple chemical species from multiple echo times. Magn Reson Med 70:155–159

    Article  PubMed  Google Scholar 

  28. Hamilton JI, Pahwa S, Adedigba J, Frankel S, O’Connor G, Thomas R, Walker JR, Killinc O, Lo WC, Batesole J, Margevicius S, Griswold M, Rajagopalan S, Gulani V, Seiberlich N (2020) Simultaneous mapping of T1 and T2 using cardiac magnetic resonance fingerprinting in a cohort of healthy subjects at 1.5T. J Magn Reson Imaging. https://doi.org/10.1002/jmri.27155

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hilbert T, Xia D, Block KT, Yu Z, Lattanzi R, Sodickson DK, Kober T, Cloos MA (2019) Magnetization transfer in magnetic resonance fingerprinting. Magn Reson Med 84:128–141

    Article  PubMed  PubMed Central  Google Scholar 

  30. Ma D, Coppo S, Chen Y, McGivney DF, Jiang Y, Pahwa S, Gulani V, Griswold MA (2017) Slice profile and B1 corrections in 2D magnetic resonance fingerprinting. Magn Reson Med 78:1781–1789

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Keerthivasan M, Bruno M, Solomon E, Benkert T, Grodzki D, Bhat H, Block T, Chandarana H (2022) Fat fraction and R2* imaging at 0.55T using a free-breathing 3D hybrid sequence. Proc. Low F. Work. Int. Soc. Magn. Reson. Med. virtual. p 34

  32. Tasdelen B, Yagiz E, Cui SX, Nayak KS (2022) Ultra-fast water/fat imaging of the abdomen at 0.55T. Proc. Low F. Work. Int. Soc. Magn. Reson. Med. virtual. p 64

  33. Tian Y, Lim Y, Nayak KS (2022) Real-time water fat imaging at 0.55T with spiral out-in-out-in sampling. In: ISMRM workshop on low field MRI, Virtual, March 2022.

  34. Shih S-F, Cui SX, Zhong X, Tasdelen B, Yagiz E, Nayak KS, Wu HH (2022) Self-gated free-breathing liver fat and R2* quantification on a high-performance 0.55T MRI system using radial acquisition and compressed sensing reconstruction. In: ISMRM workshop on low field MRI, Virtual, March 2022. p 44

  35. Dixon TW (1984) Simple proton spectroscopic imaging. Radiology 153:189–194

    Article  CAS  PubMed  Google Scholar 

  36. Glover GH, Schneider E (1991) Three-point dixon technique for true water/fat decomposition with B0 inhomogeneity correction. Magn Reson Med 18:371–383

    Article  CAS  PubMed  Google Scholar 

  37. Reeder SB, Pineda AR, Wen Z, Shimakawa A, Yu H, Brittain JH, Gold GE, Beaulieu CH, Pelc NT (2005) Iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL): application with fast spin-echo imaging. Magn Reson Med 54:636–644

    Article  PubMed  Google Scholar 

  38. Cohen O, Zhu B, Rosen MS (2018) MR fingerprinting Deep RecOnstruction NEtwork (DRONE). Magn Reson Med 80:885–894

    Article  PubMed  PubMed Central  Google Scholar 

  39. Hamilton JI, Seiberlich N (2020) Machine learning for rapid magnetic resonance fingerprinting tissue property quantification. Proc IEEE 108:69–85

    Article  Google Scholar 

  40. Cummings E, Liu Y, Jiang Y, Ropella-Panagis K, Hamilton J, Seiberlich N (2022) Simultaneous mapping of T1, T2, T2*, and fat fraction at 0.55T with Rosette MRF. Proc. Low F. Work. Int. Soc. Magn. Reson. Med. virtual

  41. Ye H, Ma D, Jiang Y, Cauley SF, Du Y, Wald LL, Griswold MA, Setsompop K (2016) Accelerating magnetic resonance fingerprinting (MRF) using t-blipped simultaneous multislice (SMS) acquisition. Magn Reson Med 75:2078–2085

    Article  CAS  PubMed  Google Scholar 

  42. Noll DC, Peltier SJ, Boada FE (1998) Simultaneous multislice acquisition using rosette trajectories (SMART): a new imaging method for functional MRI. Magn Reson Med 39:709–716

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This work is supported by NSF/CBET 1553441, NIH/NHLBI R01HL094557, R37CA263583, NIH/NHLBI R01HL163030, and Siemens Healthineers (Erlangen, Germany).

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Authors and Affiliations

  1. Department of Radiology, University of Michigan, 1150 West Medical Center Drive, Ann Arbor, MI, 48109, USA

    Yuchi Liu, Jesse Hamilton, Yun Jiang & Nicole Seiberlich

  2. Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA

    Jesse Hamilton, Yun Jiang & Nicole Seiberlich

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  1. Yuchi Liu
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Correspondence to Yuchi Liu.

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Our group receives research support from Siemens Healthineers (Erlangen, Germany).

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This study was IRB-approved. Written informed consent was obtained for all human subjects before they underwent MRI scans.

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Liu, Y., Hamilton, J., Jiang, Y. et al. Assessment of MRF for simultaneous T1 and T2 quantification and water–fat separation in the liver at 0.55 T. Magn Reson Mater Phy 36, 513–523 (2023). https://doi.org/10.1007/s10334-022-01057-9

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  • DOI: https://doi.org/10.1007/s10334-022-01057-9

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