Abdominal Radiology

, Volume 42, Issue 5, pp 1434–1443 | Cite as

Accurate simultaneous quantification of liver steatosis and iron overload in diffuse liver diseases with MRI

  • Manuela FrançaEmail author
  • Ángel Alberich-Bayarri
  • Luis Martí-Bonmatí
  • Pedro Oliveira
  • Francisca Emanuel Costa
  • Graça Porto
  • José Ramón Vizcaíno
  • Javier Sanchez Gonzalez
  • Eduardo Ribeiro
  • João Oliveira
  • Helena Pessegueiro Miranda



To evaluate the diagnostic performances of 3 Tesla multi-echo chemical shift-encoded gradient echo magnetic resonance (MECSE-MR) imaging to simultaneously quantify liver steatosis and iron overload in a wide spectrum of diffuse liver diseases having biopsy as reference standard.


MECSE-MR-acquired images were used to calculate fat fraction and iron content in a single breath-hold in 109 adult patients. Proton density fat fraction (PDFF) was prospectively estimated using complex-based data reconstruction with multipeak fat modeling. Water R2* was used to estimate iron content. Biopsy was obtained in all cases, grading liver steatosis, siderosis, inflammation, and fibrosis. Differences in PDFF and R2* values across histopathological grades were analyzed, and ROC curves analyses evaluated the MR diagnostic performance.


Calculated fat fraction measurements showed significant differences (p < 0.001) among steatosis grades, being unaffected by the presence of inflammation or fibrosis (p ≥ 0.05). A strong correlation was found between fat fraction and steatosis grade (R S = 0.718, p < 0.001). Iron deposits did not affect fat fraction quantitation (p ≥ 0.05), except in cases with severe iron overload (grade 4). A strong positive correlation was also observed between R2* measurements and iron grades (R S = 0.704, p < 0.001). Calculated R2* values were not different across grades of steatosis, inflammation, and fibrosis (p ≥ 0.05).


A MECSE-MR sequence simultaneously quantifies liver steatosis and siderosis, regardless coexisting liver inflammation or fibrosis, with high accuracy in a wide spectrum of diffuse liver disorders. This sequence can be acquired within a single breath-hold and can be implemented in the routine MR evaluation of the liver.


Fat quantification Iron overload R2* measurement Chronic liver diseases Quantitative imaging biomarkers 


Compliance with ethical standards


This work was partially funded by a research grant from the Teaching and Research Department of Centro Hospitalar do Porto (DEFI:309/12(213-DEFI/251-CES)) and from a Spanish Ministry of Health and Carlos III Health Institute funding grant (PI12/01262). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Conflict of interest

Javier Sanchez Gonzalez is employee at Philips Healthcare Iberia. Angel Alberich Bayarri and Luis Martí-Bonmatí are co-founders of QUIBIM SME. The other authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Reeder SB, Sirlin CB (2010) Quantification of liver fat with magnetic resonance imaging. Magn Reson Imaging Clin N Am 18:337–357. doi: 10.1016/j.mric.2010.08.013 CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Sirlin CB, Reeder SB (2010) Magnetic resonance imaging quantification of liver iron. Magn Reson Imaging Clin N Am 18:359–381. doi: 10.1016/j.mric.2010.08.014 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Powell EE, Ali A, Clouston AD, et al. (2005) Steatosis is a cofactor in liver injury in hemochromatosis. Gastroenterology 129:1937–1943. doi: 10.1053/j.gastro.2005.09.015 CrossRefPubMedGoogle Scholar
  4. 4.
    Ratziu V, Charlotte F, Heurtier A, et al. (2005) Sampling variability of liver biopsy in nonalcoholic fatty liver disease. Gastroenterology 128:1898–1906CrossRefGoogle Scholar
  5. 5.
    Deugnier Y, Turlin B (2007) Pathology of hepatic iron overload. WJG 13:4755–4760CrossRefGoogle Scholar
  6. 6.
    Yokoo T, Browning JD (2014) Fat and iron quantification in the liver: past, present, and future. Top Magn Reson Imaging 23:73–94. doi: 10.1097/RMR.0000000000000016 CrossRefPubMedGoogle Scholar
  7. 7.
    Gandon Y, Olivié D, Guyader D, et al. (2004) Non-invasive assessment of hepatic iron stores by MRI. Lancet 363:357–362. doi: 10.1016/S0140-6736(04)15436-6 CrossRefPubMedGoogle Scholar
  8. 8.
    Yu H, McKenzie CA, Shimakawa A, et al. (2007) Multiecho reconstruction for simultaneous water-fat decomposition and T2* estimation. J Magn Reson Imaging 26:1153–1161. doi: 10.1002/jmri.21090 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    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 60:1122–1134. doi: 10.1002/mrm.21737 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    O’Regan DP, Callaghan MF, Wylezinska-Arridge M, et al. (2008) Liver fat content and T2*: simultaneous measurement by using breath-hold multiecho MR imaging at 3.0 T—feasibility. Radiology 247:550–557. doi: 10.1148/radiol.2472070880 CrossRefPubMedGoogle Scholar
  11. 11.
    Hines CDG, Yu H, Shimakawa A, et al. (2009) T1 independent, T2* corrected MRI with accurate spectral modeling for quantification of fat: validation in a fat-water-SPIO phantom. J Magn Reson Imaging 30:1215–1222. doi: 10.1002/jmri.21957 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Meisamy S, Hines CDG, Hamilton G, et al. (2011) Quantification of hepatic steatosis with T1-independent, T2*-corrected MR imaging with spectral modeling of fat: blinded comparison with MR spectroscopy. Radiology 258:767–775. doi: 10.1148/radiol.10100708 CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Yokoo T, Bydder M, Hamilton G, et al. (2009) Nonalcoholic fatty liver disease: diagnostic and fat-grading accuracy of low-flip-angle multiecho gradient-recalled-echo MR imaging at 1.5 T. Radiology 251:67–76. doi: 10.1148/radiol.2511080666 CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Yokoo T, Shiehmorteza M, Hamilton G, et al. (2011) Estimation of hepatic proton-density fat fraction by using MR imaging at 3.0 T. Radiology 258:749–759. doi: 10.1148/radiol.10100659 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Hines CDG, Frydrychowicz A, Hamilton G, et al. (2011) T1 independent, T2* corrected chemical shift based fat-water separation with multi-peak fat spectral modeling is an accurate and precise measure of hepatic steatosis. J Magn Reson Imaging 33:873–881. doi: 10.1002/jmri.22514 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Kühn J-P, Hernando D, Mensel B, et al. (2014) Quantitative chemical shift-encoded MRI is an accurate method to quantify hepatic steatosis. J Magn Reson Imaging 39:1494–1501. doi: 10.1002/jmri.24289 CrossRefPubMedGoogle Scholar
  17. 17.
    Permutt Z, Le T-A, Peterson MR, et al. (2012) Correlation between liver histology and novel magnetic resonance imaging in adult patients with non-alcoholic fatty liver disease—MRI accurately quantifies hepatic steatosis in NAFLD. Aliment Pharmacol Ther 36:22–29. doi: 10.1111/j.1365-2036.2012.05121.x CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Tang A, Tan J, Sun M, et al. (2013) Nonalcoholic fatty liver disease: MR imaging of liver proton density fat fraction to assess hepatic steatosis. Radiology 267:422–431. doi: 10.1148/radiol.12120896 CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Idilman IS, Aniktar H, Idilman R, et al. (2013) Hepatic steatosis: quantification by proton density fat fraction with MR imaging vs. liver biopsy. Radiology 267:767–775. doi: 10.1148/radiol.13121360 CrossRefPubMedGoogle Scholar
  20. 20.
    McPherson S, Jonsson JR, Cowin GJ, et al. (2009) Magnetic resonance imaging and spectroscopy accurately estimate the severity of steatosis provided the stage of fibrosis is considered. J Hepatol 51:389–397. doi: 10.1016/j.jhep.2009.04.012 CrossRefPubMedGoogle Scholar
  21. 21.
    Parente DB, Rodrigues RS, Paiva FF, et al. (2014) Is MR spectroscopy really the best MR-based method for the evaluation of fatty liver in diabetic patients in clinical practice? PLoS ONE 9:e112574. doi: 10.1371/journal.pone.0112574.t005 CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Kühn J-P, Hernando D, del Rio AM, et al. (2012) Effect of multipeak spectral modeling of fat for liver iron and fat quantification: correlation of biopsy with MR imaging results. Radiology 265:133–142. doi: 10.1148/radiol.12112520/-/DC1 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Martí-Bonmatí L, Alberich-Bayarri A, Sánchez-González J (2011) Overload hepatitides: quanti-qualitative analysis. Abdom Imaging 37:180–187. doi: 10.1007/s00261-011-9762-5 CrossRefGoogle Scholar
  24. 24.
    Kleiner DE, Brunt EM, Van Natta M, et al. (2005) Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 41:1313–1321. doi: 10.1002/hep.20701 CrossRefPubMedGoogle Scholar
  25. 25.
    Ishak K, Baptista A, Bianchi L, et al. (1995) Histological grading and staging of chronic hepatitis. J Hepatol 22:696–699CrossRefGoogle Scholar
  26. 26.
    Mannan R (2014) A comparative evaluation of scoring systems for assessing necro-inflammatory activity and fibrosis in liver biopsies of patients with chronic viral hepatitis. J Clin Diagn Res 8:FC08-12. doi: 10.7860/JCDR/2014/8704.4718 CrossRefPubMedGoogle Scholar
  27. 27.
    Hijona E, Sánchez-González J, Alústiza JM, et al. (2012) Accurate fat fraction quantification by multiecho gradient-recalled-echo magnetic resonance at 1.5 T in rats with nonalcoholic fatty liver disease. Eur J Radiol 81:1122–1127. doi: 10.1016/j.ejrad.2011.02.065 CrossRefPubMedGoogle Scholar
  28. 28.
    Kang B-K, Yu ES, Lee SS, et al. (2012) Hepatic fat quantification: a prospective comparison of magnetic resonance spectroscopy and analysis methods for chemical-shift gradient echo magnetic resonance imaging with histologic assessment as the reference standard. Invest Radiol 47:368–375. doi: 10.1097/RLI.0b013e31824baff3 CrossRefPubMedGoogle Scholar
  29. 29.
    Tang A, Desai A, Hamilton G, et al. (2015) Accuracy of MR imaging-estimated proton density fat fraction for classification of dichotomized histologic steatosis grades in nonalcoholic fatty liver disease. Radiology 274:416–425. doi: 10.1148/radiol.14140754 CrossRefPubMedGoogle Scholar
  30. 30.
    Hamilton G, Yokoo T, Bydder M, et al. (2010) In vivo characterization of the liver fat 1H MR spectrum. NMR Biomed 24:784–790. doi: 10.1002/nbm.1622 CrossRefPubMedGoogle Scholar
  31. 31.
    Wang X, Hernando D, Reeder SB (2016) Sensitivity of chemical shift-encoded fat quantification to calibration of fat MR spectrum. Magn Reson Med 75(2):845–851. doi: 10.1002/mrm.25681 CrossRefPubMedGoogle Scholar
  32. 32.
    Chandarana H, Lim RP, Jensen JH, et al. (2009) Hepatic iron deposition in patients with liver disease: preliminary experience with breath-hold multiecho T2*-weighted sequence. Am J Roentgenol 193:1261–1267. doi: 10.2214/AJR.08.1996 CrossRefGoogle Scholar
  33. 33.
    Banerjee R, Pavlides M, Tunnicliffe EM, et al. (2014) Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease. J Hepatol 60:69–77. doi: 10.1016/j.jhep.2013.09.002 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Wood JC (2014) Use of magnetic resonance imaging to monitor iron overload. Hematol Oncol Clin N Am 28:747–764. doi: 10.1016/j.hoc.2014.04.002 CrossRefGoogle Scholar
  35. 35.
    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 250:691–697. doi: 10.1097/SLA.0b013e3181bcd6dd CrossRefPubMedGoogle Scholar
  36. 36.
    Liu C-Y, McKenzie CA, Yu H, Brittain JH, Reeder SB (2007) Fat quantification with IDEAL gradient echo imaging: correction of bias from T1 and noise. Magn Reson Med 58:354–364. doi: 10.1002/mrm.21301 CrossRefPubMedGoogle Scholar
  37. 37.
    Heba ER, Desai A, Zand KA, et al. (2016) Accuracy and the effect of possible subject-based confounders of magnitude-based MRI for estimating hepatic proton density fat fraction in adults, using MR spectroscopy as reference. J Magn Reson Imaging 43:398–406. doi: 10.1002/jmri.25006 CrossRefPubMedGoogle Scholar
  38. 38.
    Hernando D, Levin YS, Sirlin CB, Reeder SB (2014) Quantification of liver iron with MRI: state of the art and remaining challenges. J Magn Reson Imaging 40:1003–1021. doi: 10.1002/jmri.24584 CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Storey P, Thompson AA, Carqueville CL, et al. (2007) R2* imaging of transfusional iron burden at 3 T and comparison with 1.5 T. J Magn Reson Imaging 25:540–547. doi: 10.1002/jmri.20816 CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Meloni A, Positano V, Keilberg P, et al. (2012) Feasibility, reproducibility, and reliability for the T*2 iron evaluation at 3 T in comparison with 1.5 T. Magn Reson Med 68:543–551. doi: 10.1002/mrm.23236 CrossRefPubMedGoogle Scholar
  41. 41.
    Tyagi A, Yeganeh O, Levin Y, et al. (2015) Intra- and inter-examination repeatability of magnetic resonance spectroscopy, magnitude-based MRI, and complex-based MRI for estimation of hepatic proton density fat fraction in overweight and obese children and adults. Abdom Imaging 40:3070–3077. doi: 10.1007/s00261-015-0542-5 CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    Hernando D, Sharma SD, Aliyari Ghasabeh M et al (2016) Multisite, multivendor validation of the accuracy and reproducibility of proton-density fat-fraction quantification at 1.5 T and 3 T using a fat-water phantom. Magn Reson Med. doi:  10.1002/mrm.26228.CrossRefGoogle Scholar
  43. 43.
    Alam MH, Auger D, McGill LA, et al. (2016) Comparison of 3 T and 1.5 T for T2* magnetic resonance of tissue iron. J Cardiovasc Magn Reson 18:40. doi: 10.1186/s12968-016-0259-9 CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Hernando D, Kramer JH, Reeder SB (2013) Multipeak fat-corrected complex R2* relaxometry: theory, optimization, and clinical validation. Magn Reson Med 70:1319–1331. doi: 10.1002/mrm.24593 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Manuela França
    • 1
    Email author
  • Ángel Alberich-Bayarri
    • 2
  • Luis Martí-Bonmatí
    • 2
  • Pedro Oliveira
    • 3
    • 4
  • Francisca Emanuel Costa
    • 5
  • Graça Porto
    • 6
    • 7
    • 8
  • José Ramón Vizcaíno
    • 5
  • Javier Sanchez Gonzalez
    • 9
  • Eduardo Ribeiro
    • 1
  • João Oliveira
    • 1
  • Helena Pessegueiro Miranda
    • 4
    • 10
  1. 1.Imaging DepartmentCentro Hospitalar do PortoPortoPortugal
  2. 2.Radiology DepartmentHospital Universitario y Politécnico La Fe and Biomedical Imaging Research Group (GIBI230)ValenciaSpain
  3. 3.Population Studies Department, Institute of Biomedical Sciences Abel Salazar (ICBAS)University of PortoPortoPortugal
  4. 4.Epidemiology Research Unit (EPIUnit)Institute of Public Health of the University of PortoPortoPortugal
  5. 5.Pathology DepartmentCentro Hospitalar do PortoPortoPortugal
  6. 6.Hematology DepartmentCentro Hospitalar do PortoPortoPortugal
  7. 7.I3S, Instituto de Investigação e Inovação em SaúdePortoPortugal
  8. 8.IBMC, Institute for Molecular and Cell BiologyPortoPortugal
  9. 9.MR Clinical SciencePhilips HealthcareMadridSpain
  10. 10.Liver and Pancreas Transplantation Unit and Medicine DepartmentCentro Hospitalar do PortoPortoPortugal

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