A hybrid (iron–fat–water) phantom for liver iron overload quantification in the presence of contaminating fat using magnetic resonance imaging

  • Nazanin Mobini
  • Malakeh Malekzadeh
  • Hamidreza Haghighatkhah
  • Hamidreza Saligheh RadEmail author
Research Article



Assessment of iron content in the liver is crucial for diagnosis/treatment of iron-overload diseases. Nonetheless, T2*-based methods become challenging when fat and iron are simultaneously present. This study proposes a phantom design concomitantly containing various concentrations of iron and fat suitable for devising accurate simultaneous T2* and fat quantification technique.

Materials and methods

A 46-vial iron–fat–water phantom with various iron concentrations covering clinically relevant T2* relaxation time values, from healthy to severely overloaded liver and wide fat percentages ranges from 0 to 100% was prepared. The phantom was constructed using insoluble iron (II, III) oxide powder containing microscale particles. T2*-weighted imaging using multi-gradient-echo (mGRE) sequence, and chemical shift imaging spin-echo (CSI-SE) Magnetic Resonance Spectroscopy (MRS) data were considered for the analysis. T2* relaxation times and fat fractions were extracted from the MR signals to explore the effects of fat and iron overload.


Size distribution of iron oxide particles for Magnetite fits with a lognormal function with a mean size of about 1.17 µm. Comparison of FF color maps, estimated from bi- and mono-exponential model indicated that single-T2* fitting model resulted in lower NRMSD. Therefore, T2* values from the mono-exponential signal equation were used and expressed the relationship between relaxation time value across all iron (Fe) and fat concentration as \({\text{Fe}} = - 28.02 + \frac{302.84}{{T2^{*} }} - 0.045\,{\text{FF}}\), with R-squared = 0.89.


The proposed phantom design with microsphere iron particles closely simulated the single-T2* behavior of fatty iron-overloaded liver in vivo.


Iron–fat–water phantom Magnetic resonance imaging Iron overload Fatty liver 



The authors would like to thank Hamid Emadi for help in preparing the phantom, Anahita Fathi Kazerooni for her scientific editing and comments on the manuscript. Imaging for this work was performed at National Brain Mapping Laboratory (NBML).

Author contributions

NM was responsible for study conception and design, data collection, analysis and interpretation of data, and drafting the manuscript; MM helped with the interpretation of data and critical revision; HH designed the study conception and acquisition of data; HS was responsible for protocol/project development of the framework and critical revision.


This phantom study has been supported by Tehran University of Medical Sciences & Health Services Grants 27331 and 32965.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.


  1. 1.
    Gupta CP (2014) Role of iron (Fe) in body. IOSR J Appl Chem (IOSR-JAC) 7:38–46CrossRefGoogle Scholar
  2. 2.
    Andrews NC (1999) Disorders of iron metabolism. N Engl J Med 341(26):1986–1995CrossRefGoogle Scholar
  3. 3.
    Brittenham GM, Badman DG (2003) Noninvasive measurement of iron: report of an NIDDK workshop. Blood 101(1):15–19CrossRefGoogle Scholar
  4. 4.
    Batts KP (2007) Iron overload syndromes and the liver. Mod Pathol 20(1s):S31CrossRefGoogle Scholar
  5. 5.
    Yokoo T, Yuan Q, Sénégas J, Wiethoff AJ, Pedrosa I (2015) Quantitative R2* MRI of the liver with Rician noise models for evaluation of hepatic iron overload: Simulation, phantom, and early clinical experience. J Magn Reson Imaging 42(6):1544–1559CrossRefGoogle Scholar
  6. 6.
    Horng DE, Hernando D, Reeder SB (2017) Quantification of liver fat in the presence of iron overload. J Magn Reson Imaging 45(2):428–439CrossRefGoogle Scholar
  7. 7.
    Yokoo T, Browning JD (2014) Fat and iron quantification in the liver: past, present, and future. Top Magn Reson Imaging 23(2):73–94CrossRefGoogle Scholar
  8. 8.
    Kühn J-P, Meffert P, Heske C, Kromrey M-L, Schmidt CO, Mensel B, Völzke H, Lerch MM, Hernando D, Mayerle J (2017) Prevalence of fatty liver disease and hepatic iron overload in a Northeastern German population by using quantitative MR imaging. Radiology 28(3):706–716CrossRefGoogle Scholar
  9. 9.
    Tipirneni-Sajja A, Krafft AJ, Loeffler RB, Song R, Bahrami A, Hankins JS, Hillenbrand CM (2019) Autoregressive moving average modeling for hepatic iron quantification in the presence of fat. J Magn Reson Imaging 50:1620–1632. CrossRefPubMedGoogle Scholar
  10. 10.
    Lidbury JA (2017) Getting the most out of liver biopsy. Vet Clin Small Animal Pract 47(3):569–583CrossRefGoogle Scholar
  11. 11.
    Ratziu V, Charlotte F, Heurtier A, Gombert S, Giral P, Bruckert E, Grimaldi A, Capron F, Poynard T, Group LS (2005) Sampling variability of liver biopsy in nonalcoholic fatty liver disease. Gastroenterology 128(7):1898–1906CrossRefGoogle Scholar
  12. 12.
    Li TQ, Aisen AM, Hindmarsh T (2004) Assessment of hepatic iron content using magnetic resonance imaging. Acta Radiol 45(2):119–129CrossRefGoogle Scholar
  13. 13.
    Sirlin CB, Reeder SB (2010) Magnetic resonance imaging quantification of liver iron. Magn Reson Imaging Clin N Am 18(3):359–381CrossRefGoogle Scholar
  14. 14.
    Bowen CV, Zhang X, Saab G, Gareau PJ, Rutt BK (2002) Application of the static dephasing regime theory to superparamagnetic iron-oxide loaded cells. Magn Reson Med 48(1):52–61CrossRefGoogle Scholar
  15. 15.
    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(5):1122–1134CrossRefGoogle Scholar
  16. 16.
    Clark PR, Chua-anusorn W, St. Pierre TG (2003) Bi-exponential proton transverse relaxation rate (R 2) image analysis using RF field intensity-weighted spin density projection: potential for R 2 measurement of iron-loaded liver. Magn Reson Imaging 21(5):519–530CrossRefGoogle Scholar
  17. 17.
    Bernard CP, Liney GP, Manton DJ, Turnbull LW, Langton CM (2008) Comparison of fat quantification methods: a phantom study at 30T. J Magn Reson imaging 27(1):192–197CrossRefGoogle Scholar
  18. 18.
    Hines CD, Yu H, Shimakawa A, McKenzie CA, Brittain JH, Reeder SB (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 JMRI 30(5):1215–1222CrossRefGoogle Scholar
  19. 19.
    Reeder SB, Hernando D, Sharma S (2016) Phantom for iron and fat quantification magnetic resonance imaging. United States PatentGoogle Scholar
  20. 20.
    McMullan D (1995) Scanning electron microscopy 1928–1965. Scanning 17(3):175–185CrossRefGoogle Scholar
  21. 21.
    Reinoso RF, Telfe BA, Rowland M (1997) Tissue water content in rats measured by desiccation. J Pharmacol Toxicol Methods 38(2):87–92CrossRefGoogle Scholar
  22. 22.
    Hernando D, Cook RJ, Diamond C, Reeder SB (2013) Magnetic susceptibility as a B0 field strength independent MRI biomarker of liver iron overload. Magn Reson Med 70(3):648–656CrossRefGoogle Scholar
  23. 23.
    Fukuzawa K, Hayashi T, Takahashi J, Yoshihara C, Tano M, Ji Kotoku, Saitoh S (2017) Evaluation of six-point modified dixon and magnetic resonance spectroscopy for fat quantification: a fat-water-iron phantom study. Radiol Phys Technol 10(3):349–358CrossRefGoogle Scholar
  24. 24.
    Bydder M, Hamilton G, de Rochefort L, Desai A, Heba ER, Loomba R, Schwimmer JB, Szeverenyi NM, Sirlin CB (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):e3843CrossRefGoogle Scholar
  25. 25.
    Storey P, Thompson AA, Carqueville CL, Wood JC, de Freitas RA, Rigsby CK (2007) R2* imaging of transfusional iron burden at 3 T and comparison with 1.5 T. J Magn Reson Imaging 25(3):540–547CrossRefGoogle Scholar
  26. 26.
    Meloni A, Positano V, Keilberg P, De Marchi D, Pepe P, Zuccarelli A, Campisi S, Romeo MA, Casini T, Bitti PP, Gerardi C, Lai ME, Piraino B, Giuffrida G, Secchi G, Midiri M, Lombardi M, Pepe A (2012) Feasibility, reproducibility, and reliability for the T* 2 iron evaluation at 3 T in comparison with 1.5 T. Magn Reson Med 68(2):543–551CrossRefGoogle Scholar
  27. 27.
    Yamamura J, Keller S, Grosse R, Schoennagel B, Nielsen P, Wang ZJ, Graessner J, Kooijman H, Adam G, Fischer R (2016) Iron measurements by quantitative MRI-R2* at 3.0 and 1.5 T. In: ISMRMGoogle Scholar
  28. 28.
    Doyle EK, Toy K, Valdez B, Chia JM, Coates T, Wood JC (2017) Ultra-short echo time images quantify high liver iron. Magn Reson Med. CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Krafft AJ, Loeffler RB, Song R, Tipirneni-Sajja A, McCarville MB, Robson MD, Hankins JS, Hillenbrand CM (2017) Quantitative ultrashort echo time imaging for assessment of massive iron overload at 1.5 and 3 Tesla. Magn Reson Med. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) 2019

Authors and Affiliations

  1. 1.Department of Medical Physics and Biomedical Engineering, School of MedicineTehran University of Medical Sciences (TUMS)TehranIran
  2. 2.Quantitative MR Imaging and Spectroscopy Group (QMISG), Research Center for Cellular and Molecular Imaging, Institute for Advanced Medical Technologies, Imam Khomeini Hospital ComplexTehran University of Medical SciencesTehranIran
  3. 3.Medical Physics Department, School of MedicineIran University of Medical Sciences (IUMS)TehranIran
  4. 4.Department of Radiology, Shohada-e Tajrish HospitalShahid Beheshti University of Medical Sciences (SBMU)TehranIran

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