Abdominal Imaging

, Volume 37, Issue 2, pp 180–187 | Cite as

Overload hepatitides: quanti-qualitative analysis

  • Luis Martí-Bonmatí
  • Angel Alberich-Bayarri
  • Javier Sánchez-González


Diffuse liver diseases have a definitive radiological importance due to the ability of MR imaging to demonstrate abnormalities before the patient is symptomatic or the liver damage is advanced. Biopsy procedures are invasive, may lead to complications and have a sample bias. Imaging biomarkers target to fat, water, and iron tissue concentrations may be considered as hepatic virtual biopsies. There is a need to identify a rapid and practicable method to accurately quantify liver steatosis, differentiate steatohepatitis from simple steatosis, grade the necroinflammatory activity, calculate the liver iron burden and monitor overload progression. MR is used in the evaluation of diffuse liver disorders with accurate approaches such as the use of chemical shift, Dixon vector analysis, turbo spin echo fat suppression, and T2* gradient echo techniques. These methods are influenced by some factors like proportional ambiguity, T1 and T2* effects on signal decay, adding a significant bias in the combined fat–water–iron quantification. A GRE multi-echo chemical shift sequence was configured to independently calculate fat, water, and iron parametric liver images. It is now necessary to conduct a pilot project in order to validate this method in a group of subjects without and with different grades of fat, water, and iron liver changes.

Key words

Fatty liver Iron overload Magnetic resonance imaging Quantitative evaluation Imaging biomarkers 


  1. 1.
    Martí-Bonmatí L, Talens A, del Olmo J, et al. (1993) Chronic hepatitis and cirrhosis: evaluation by means of MR imaging with histologic correlation. Radiology 188:37–43PubMedGoogle Scholar
  2. 2.
    Yu H, Shimakawa A, McKenzie CA, et al. (2008) Multiecho water–fat separation and simultaneous R2* estimation with multifrequency fat spectrum modeling. Magn Reson Med 60:1122–1134PubMedCrossRefGoogle Scholar
  3. 3.
    Springer F, Machann J, Claussen CD, Schick F, Schwenzer NF (2010) Liver fat content determined by magnetic resonance imaging and spectroscopy. World J Gastroenterol 16:1560–1566PubMedCrossRefGoogle Scholar
  4. 4.
    Qayyum A, Goh JS, Kakar S, et al. (2005) Accuracy of liver fat quantification at MR imaging: comparison of out-of-phase gradient-echo and fat-saturated fast spin-echo techniques—initial experience. Radiology 237:507–511PubMedCrossRefGoogle Scholar
  5. 5.
    Hussain HK, Chenevert TL, Londy FJ, et al. (2005) Hepatic fat fraction: MR imaging for quantitative measurement and display–early experience. Radiology 237:1048–1055PubMedCrossRefGoogle Scholar
  6. 6.
    Fishbein MH, Gardner KG, Potter CJ, Schmalbrock P, Smith MA (1997) Introduction of fast MR imaging in the assessment of hepatic steatosis. Magn Reson Imaging 15:287–293PubMedCrossRefGoogle Scholar
  7. 7.
    Westphalen AC, Qayyum A, Yeh BM, et al. (2007) Liver fat: effect of hepatic iron deposition on evaluation with opposed-phase MR imaging. Radiology 242:450–455PubMedCrossRefGoogle Scholar
  8. 8.
    Chitturi S, George J (2003) Interaction of iron, insulin resistance, and nonalcoholic steatohepatitis. Curr Gastroenterol Rep 5:18–25PubMedCrossRefGoogle Scholar
  9. 9.
    El-Badry AM, Breitenstein S, Jochum W, et al. (2009) Assessment of hepatic steatosis by expert pathologists: the end of a gold standard. Ann Surg 250:691–697PubMedCrossRefGoogle Scholar
  10. 10.
    Lim RP, Tuvia K, Hajdu CH, et al. (2010) Quantification of hepatic iron deposition in patients with liver disease: comparison of chemical shift imaging with single-echo T2*-weighted imaging. AJR Am J Roentgenol 194:1288–1295PubMedCrossRefGoogle Scholar
  11. 11.
    Niederau C, Fischer R, Sonnenberg A, et al. (1985) Survival and causes of death in cirrhotic and noncirrhotic patients with primary hemochromatosis. N Engl J Med 313:1256–1262PubMedCrossRefGoogle Scholar
  12. 12.
    Villeneuve JP, Bilodeau M, Lepage R, Côté J, Lefebvre M (1996) Variability in hepatic iron concentration measurement from needle-biopsy specimens. J Hepatol 25:172–177PubMedCrossRefGoogle Scholar
  13. 13.
    Zhang X, Tengowski M, Fasulo L, Botts S (2004) Measurement of fat/water ratios in rat liver using 3D three-point Dixon MRI. Magn Reson Med 51:697–702PubMedCrossRefGoogle Scholar
  14. 14.
    Reeder SB, McKenzie CA, Pineda AR, et al. (2007) Water–fat separation with IDEAL gradient-echo imaging. J Magn Reson Imaging 25:644–652PubMedCrossRefGoogle Scholar
  15. 15.
    Machann J, Thamer C, Schnoedt B, et al. (2006) Hepatic lipid accumulation in healthy subjects: a comparative study using spectral fat-selective MRI and volume-localized 1H-MR spectroscopy. Magn Reson Med 55:913–917PubMedCrossRefGoogle Scholar
  16. 16.
    Boll DT, Marin D, Redmon GM, Zink SI, Merkle EM (2010) Pilot study assessing differentiation of steatosis hepatis, hepatic iron overload, and combined disease using two-point Dixon MRI at 3 T: in vitro and in vivo results of a 2D decomposition technique. AJR Am J Roentgenol 194:964–971PubMedCrossRefGoogle Scholar
  17. 17.
    Liu CY, McKenzie CA, Yu H, Brittain JH, Reeder SB (2007) Fat quantification with IDEAL gradient echo imaging: correction of bias from T(1) and noise. Magn Reson Med 58:354–364PubMedCrossRefGoogle Scholar
  18. 18.
    Longo R, Pollesello P, Ricci C, et al. (1995) Proton MR spectroscopy in quantitative in vivo determination of fat content in human liver steatosis. J Magn Reson Imaging 5:281–285PubMedCrossRefGoogle Scholar
  19. 19.
    Marsman HA, van Werven JR, Nederveen AJ, et al. (2010) Noninvasive quantification of hepatic steatosis in rats using 3.0 T 1H-magnetic resonance spectroscopy. J Magn Reson Imaging 32:148–154PubMedCrossRefGoogle Scholar
  20. 20.
    Yu H, McKenzie CA, Shimakawa A, et al. (2007) Multiecho reconstruction for simultaneous water–fat decomposition and T2* estimation. J Magn Reson Imaging 26:1153–1161PubMedCrossRefGoogle Scholar
  21. 21.
    Van Huffel S, Chen H, Decanniers C, Van Hecke P (1994) Algorithm for time-domain NMR data fitting based on total least squares. J Magn Reson A 110:228–237CrossRefGoogle Scholar
  22. 22.
    Marti-Bonmati L, Alberich-Bayarri A, García-Martí G, et al. Imaging biomarkers, quantitative imaging and bioengineering. Radiología (in press).Google Scholar
  23. 23.
    Hussein R, Engelmann U, Schroeter A, Meinzer HP (2004) DICOM structured reporting: part 2. Problems and challenges in implementation for PACS workstations. Radiographics 24:897–909PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Luis Martí-Bonmatí
    • 1
    • 2
  • Angel Alberich-Bayarri
    • 1
  • Javier Sánchez-González
    • 3
  1. 1.Radiology DepartmentHospital QuirónValenciaSpain
  2. 2.Department of MedicineUniversity of ValenciaValenciaSpain
  3. 3.Philips Cuidados de la SaludMadridSpain

Personalised recommendations