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Low-level fat fraction quantification at 3 T: comparative study of different tools for water–fat reconstruction and MR spectroscopy

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Abstract

Objectives

Chemical Shift Encoded Magnetic Resonance Imaging (CSE-MRI)-based quantification of low-level (< 5% of proton density fat fraction—PDFF) fat infiltration requires highly accurate data reconstruction for the assessment of hepatic or pancreatic fat accumulation in diagnostics and biomedical research.

Materials and methods

We compare three software tools available for water/fat image reconstruction and PDFF quantification with MRS as the reference method. Based on the algorithm exploited in the tested software, the accuracy of fat fraction quantification varies. We evaluate them in phantom and in vivo MRS and MRI measurements.

Results

The signal model of Intralipid 20% emulsion used for phantoms was established for 3 T and 9.4 T fields. In all cases, we noticed a high coefficient of determination (R-squared) between MRS and MRI–PDFF measurements: in phantoms <0.9924–0.9990>; and in vivo <0.8069–0.9552>. Bland–Altman analysis was applied to phantom and in vivo measurements.

Discussion

Multi-echo MRI in combination with an advanced algorithm including multi-peak spectrum modeling appears as a valuable and accurate method for low-level PDFF quantification over large FOV in high resolution, and is much faster than MRS methods. The graph-cut algorithm (GC) showed the fewest water/fat swaps in the PDFF maps, and hence stands out as the most robust method of those tested.

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References

  1. Dixon W (1984) Simple proton spectroscopic imaging. Radiology 153:189–194

    CAS  PubMed  Google Scholar 

  2. Glover G (1991) Multipoint Dixon technique for water and fat proton and susceptibility imaging. J Magn Reson Imaging 1(5):521–530

    CAS  PubMed  Google Scholar 

  3. Glover G, Schneider E (1991) Three-point Dixon technique for true water/fat decomposition with B0 field inhomogeneity correction. Magn Reson Med 18(2):371–383

    CAS  PubMed  Google Scholar 

  4. Hardy P, Hinks R (1995) Separation of fat and water in fast spin-echo MR imaging with the three-point Dixon technique. J Magn Reson Imaging 5(2):181–185

    CAS  PubMed  Google Scholar 

  5. Xiang Q, An L (1997) Water-fat imaging with direct phase encoding. J Magn Reson Imaging 7(6):1002–1015

    CAS  PubMed  Google Scholar 

  6. Rybicki F, Chung T, Reid J, Jaramillo D, Mulkern R, Ma J (2001) Fast three-point dixon MR imaging using low-resolution images for phase correction: a comparison with chemical shift selective fat suppression for pediatric musculoskeletal imaging. AJR Am J Roentgenol 177(5):1019–1023

    CAS  PubMed  Google Scholar 

  7. Ma J, Singh S, Kumar A, Leeds N, Broemeling L (2002) Method for efficient fast spin echo Dixon imaging. Magn Reson Med 48(6):1021–1027

    PubMed  Google Scholar 

  8. Reeder S, Wen Z, Yu H, Pineda A, Gold G, Markl M, Pelc N (2004) Multicoil Dixon chemical species separation with an iterative least-squares estimation method. Magn Reson Med 51(1):35–45

    CAS  PubMed  Google Scholar 

  9. Reeder S, Pineda A, Wen Z, Shimakawa A, Yu H, Brittain J, Gold G, Beaulieu C, Pelc N (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(3):636–644

    PubMed  Google Scholar 

  10. Pineda A, Reeder S, Wen Z, Pelc N (2005) Cramér–Rao bounds for three-point decomposition of water and fat. Magn Reson Med 54(3):625–635

    PubMed  Google Scholar 

  11. Yu H, McKenzie C, Shimakawa A, Vu A, Brau A, Beatty P, Pineda A, Brittain J, Reeder S (2007) Multiecho reconstruction for simultaneous water-fat decomposition and T2* estimation. J Magn Reson Imaging 26(4):1153–1161

    PubMed  Google Scholar 

  12. Yu H, Shimakawa A, McKenzie C, Brodsky E, Brittain J, Reeder S (2008) Multiecho water-fat separation and simultaneous R2* estimation with multifrequency fat spectrum modeling. Magn Reson Med 60(5):1122–1134

    PubMed  PubMed Central  Google Scholar 

  13. Hernando D, Haldar J, Sutton B, Ma J, Kellman P, Liang Z (2008) Joint estimation of water/fat images and field inhomogeneity map. Magn Reson Med 59(3):571–580

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Hernando D, Kellman P, Haldar J, Liang Z (2010) Robust water/fat separation in the presence of large field inhomogeneities using a graph cut algorithm. Magn Reson Med 63(1):79–90

    PubMed  PubMed Central  Google Scholar 

  15. Tsao J, Jiang Y (2013) Hierarchical IDEAL: fast, robust, and multiresolution separation of multiple chemical species from multiple echo times. Magn Reson Med 70(1):155–159

    PubMed  Google Scholar 

  16. Haase A, Frahm J, Hänicke W, Matthaei D (1985) 1H NMR chemical shift selective (CHESS) imaging. Phys Med Biol 30(4):341–344

    CAS  PubMed  Google Scholar 

  17. Schricker A, Pauly J, Kurhanewicz J, Swanson M, Vigneron D (2001) Dualband spectral-spatial RF pulses for prostate MR spectroscopic imaging. Magn Reson Med 46(6):1079–1087

    CAS  PubMed  Google Scholar 

  18. Krinsky G, Rofsky N, Weinreb J (1996) Nonspecificity of short inversion time inversion recovery (STIR) as a technique of fat suppression: pitfalls in image interpretation. AJR Am J Roentgenol 166(3):523–526

    CAS  PubMed  Google Scholar 

  19. Bloembergen N, Purcell E, Pound R (1948) Relaxation effects in nuclear magnetic resonance absorption. Phys. Rev. 73(7):679–712

    CAS  Google Scholar 

  20. Proctor W, Yu F (1950) The dependence of a nuclear magnetic resonance frequency upon chemical compound. Phys Rev 77:717

    CAS  Google Scholar 

  21. Liu C, McKenzie C, Yu H, Brittain J, Reeder S (2007) Fat quantification with IDEAL gradient echo imaging: correction of bias from T(1) and noise. Magn Reson Med 58:354–364

    PubMed  Google Scholar 

  22. Ren J, Dimitrov I, Sherry A, Malloy C (2008) Composition of adipose tissue and marrow fat in humans by 1H NMR at 7 Tesla. J Lipid Res 49(9):2055–2062

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Hamilton G, Smith DJ, Bydder M, Nayak K, Hu H (2011) MR properties of brown and white adipose tissues. J Magn Reson Imaging 34(2):468–473

    PubMed  PubMed Central  Google Scholar 

  24. Reeder S, Hu H, Sirlin C (2012) Proton density fat-fraction: a standardized MR-based biomarker of tissue fat concentration. J Magn Reson Imaging 36(5):1011–1014

    PubMed  PubMed Central  Google Scholar 

  25. Krssák M, Hofer H, Wrba F, Meyerspeer M, Brehm A, Lohninger A, Steindl-Munda P, Moser E, Ferenci P, Roden M (2010) Non-invasive assessment of hepatic fat accumulation in chronic hepatitis C by 1H magnetic resonance spectroscopy. Eur J Radiol 74(3):e60–e66

    PubMed  Google Scholar 

  26. Hájek M, Dezortová M, Wagnerová D, Skoch A, Voska L, Hejlová I, Trunečka P (2011) MR spectroscopy as a tool for in vivo determination of steatosis in liver transplant recipients. MAGMA 24(5):297–304

    PubMed  Google Scholar 

  27. Livingstone R, Begovatz P, Kahl S, Nowotny B, Strassburger K, Giani G, Bunke J, Roden M, Hwang J (2014) Initial clinical application of modified Dixon with flexible echo times: hepatic and pancreatic fat assessments in comparison with 1H MRS. Magn Reson Mater Phy 27:397–405

    CAS  Google Scholar 

  28. Kim H, Taksali S, Dufour S, Befroy D, Goodman T, Petersen K, Shulman G, Caprio S, Constable R (2008) Comparative MR study of hepatic fat quantification using single-voxel proton spectroscopy, two-point dixon and three-point IDEAL. Magn Reson Med 59(3):521–527

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Kukuk G, Hittatiya K, Sprinkart A, Eggers H, Gieseke J, Block W, Moeller P, Willinek W, Spengler U, Trebicka J, Fischer H, Schild H, Träber F (2015) Comparison between modified Dixon MRI techniques, MR spectroscopic relaxometry, and different histologic quantification methods in the assessment of hepatic steatosis. Eur Radiol 25(10):2869–2879

    PubMed  Google Scholar 

  30. Ishizaka K, Oyama N, Mito S, Sugimori H, Nakanishi M, Okuaki T, Shirato H, Terae S (2011) Comparison of 1H MR spectroscopy, 3-point DIXON, and multi-echo gradient echo for measuring hepatic fat fraction. Magn Reson Med Sci 10(1):41–48

    PubMed  Google Scholar 

  31. Hong C, Mamidipalli A, Hooker J, Hamilton G, Wolfson T, Chen D, Fazeli DS, Middleton M, Reeder S, Loomba R, Sirlin C (2018) MRI proton density fat fraction is robust across the biologically plausible range of triglyceride spectra in adults with nonalcoholic steatohepatitis. J Magn Reson Imaging 47(4):995–1002

    PubMed  Google Scholar 

  32. Yokoo T, Serai S, Pirasteh A, Bashir M, Hamilton G, Hernando D, Hu H, Hetterich H, Kühn J, Kukuk G, Loomba R, Middleton M, Obuchowski N, Song J, Tang A, Wu X, Reeder S, Sirlin C (2018) Linearity, bias, and precision of hepatic proton density fat fraction measurements by using MR imaging: a meta-analysis. Radiology 286(2):486–498

    PubMed  Google Scholar 

  33. Bydder M, Hamilton G, de Rochefort L, Desai A, Heba E, Loomba R, Schwimmer J, Szeverenyi N, Sirlin C (2018) Sources of systematic error in proton density fat fraction (PDFF) quantification in the liver evaluated from magnitude images with different numbers of echoes. NMR Biomed 31(1):e3843

    Google Scholar 

  34. FatWater12 ISMRM toolbox (2012) [Online]. https://www.ismrm.org/workshops/FatWater12/data.htm

  35. Smith D, Berglund J, Kullberg J, Ahlstrm HAM, Welch E (2013) Optimization of fat–water separation algorithm selection and options using image-based metrics with validation by ISMRM fat–water challenge datasets. In: Proceedings of international society for magnetic resonance in medicine 21, Salt-Lake City, USA, 2013

  36. Tsao J, Jiang Y (2008) Hierarchical IDEAL—robust water–fat separation at high field by multiresolution field map estimation. In: Proceeding of the 16th Annual Meeting of ISMRM, Toronto, Canada

  37. An L, Xiang Q (2001) Chemical shift imaging with spectrum modeling. Magn Reson Med 46(1):126–130

    CAS  PubMed  Google Scholar 

  38. Ma J (2004) Breath-hold water and fat imaging using a dual-echo two-point Dixon technique with an efficient and robust phase-correction algorithm. Magn Reson Med 52(2):415–419

    PubMed  Google Scholar 

  39. Yu H, Reeder S, Shimakawa A, Brittain J, Pelc N (2005) Field map estimation with a region growing scheme for iterative 3-point water-fat decomposition. Magn Reson Med 54(4):1032–1039

    PubMed  Google Scholar 

  40. Lu W, Hargreaves B (2008) Multiresolution field map estimation using golden section search for water-fat separation. Magn Reson Med 60(1):236–244

    PubMed  Google Scholar 

  41. Sharma S, Hu H, Nayak K (2012) Accelerated water-fat imaging using restricted subspace field map estimation and compressed sensing. Magn Reson Med 67(3):650–659

    PubMed  Google Scholar 

  42. Haase A, Frahm J, Matthaei D, Hanicke W, Merboldt K-D (1986) FLASH imaging: rapid NMR imaging using low flip angle pulses. J Magn Reson 67:258–266

    CAS  Google Scholar 

  43. Pineda N, Sharma P, Xu Q, Hu X, Vos M, Martin D (2009) Measurement of hepatic lipid: high-speed T2-corrected multiecho acquisition at 1H MR spectroscopy—a rapid and accurate technique. Radiology 252(2):568–576

    PubMed  Google Scholar 

  44. Frahm J, Merboldt K-D, Hänicke W (1987) Localized proton spectroscopy using stimulated echoes. J Magn Reson 72:502–508

    CAS  Google Scholar 

  45. Yu H, Shimakawa A, Hines C, McKenzie C, Hamilton G, Sirlin C, Brittain J, Reeder S (2011) Combination of complex-based and magnitude-based multiecho water–fat separation for accurate quantification of fat-fraction. Magn Reson Med 66(1):199–206

    PubMed  PubMed Central  Google Scholar 

  46. Pijnappel W, van den Boogaart A, de Beer R, van Ormondt D (1992) SVD-based quantification of magnetic resonance signals. J Magn Reson 97:122–134

    Google Scholar 

  47. Stefan D, Di Cesare F, Andrasescu A, Popa E, Lazariev A, Vescovo E, Strbak O, Williams S, Starcuk Z, Cabanas M, van Ormondt D, Graveron-Demilly D (2009) Quantitation of magnetic resonance spectroscopy signals: the jMRUI software package. Meas Sci Technol 20:104035

  48. Cavassila S, Fenet B, van den Boogaart A, Rémy C, Briguet C, Graveron-Demilly D (1997) ER-Filter: a preprocessing technique to improve the performance of SVD-based quantitation methods. J Magn Reson Anal 3:87–92

    Google Scholar 

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Acknowledgements

This work was partially supported by the European Commission and Ministry of Education, Youth, and Sports (projects No. CZ.1.05/2.1.00/01.0017, LO1212; No. 7AMB18AT023; 8J18AT023; AKTION AUT-CZE # 74p6), the Czech Academy of Sciences (project No. MSM100651801), Austrian Federal Ministry of Education, Science and Research; Contract grant number: BMWFW WTZ Mobility, CZ09-2019.

Funding

This study was funded by the European Commission and Ministry of Education, Youth, and Sports (projects No. CZ.1.05/2.1.00/01.0017, LO1212; No. 7AMB18AT023; 8J18AT023; AKTION AUT-CZE # 74p6), the Czech Academy of Sciences (project No. MSM100651801).

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Authors and Affiliations

  1. Institute of Scientific Instruments of the CAS, Kralovopolska 147, 612 64, Brno, Czech Republic

    Radim Kořínek & Zenon Starčuk Jr.

  2. Department of Biomedical Imaging and Image-Guided Therapy, High-Field MR Centre, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria

    Martin Gajdošík, Siegfried Trattnig & Martin Krššák

  3. Department of Biomedical Engineering, Columbia University Fu Foundation School of Engineering and Applied Science, 1210 Amsterdam Ave, New York, NY, 10027, USA

    Martin Gajdošík

  4. Christian Doppler Laboratory for Clinical Molecular Imaging, MOLIMA, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria

    Siegfried Trattnig & Martin Krššák

  5. Division of Endocrinology and Metabolism, Department of Medicine III, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria

    Martin Krššák

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  1. Radim Kořínek
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  2. Martin Gajdošík
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Contributions

RK: study conception and design, acquisition of data, analysis and interpretation of data, drafting of manuscript. MG: critical revision. ST: critical revision. ZS jr.: critical revision, drafting of manuscript. MK: study conception and design, drafting of manuscript.

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Correspondence to Radim Kořínek.

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Author Radim Kořínek declares that he has no conflict of interest. Author Martin Gajdošík declares that he has no conflict of interest. Author Martin Gajdošík declares that he has no conflict of interest. Author Siegfried Trattnig declares that he has no conflict of interest. Author Zenon Starčuk jr. declares that he has no conflict of interest. Author Martin Krššák declares that he has no conflict of interest.

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Kořínek, R., Gajdošík, M., Trattnig, S. et al. Low-level fat fraction quantification at 3 T: comparative study of different tools for water–fat reconstruction and MR spectroscopy. Magn Reson Mater Phy 33, 455–468 (2020). https://doi.org/10.1007/s10334-020-00825-9

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  • DOI: https://doi.org/10.1007/s10334-020-00825-9

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