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Magnetic Resonance Materials in Physics, Biology and Medicine
Published in: Magnetic Resonance Materials in Physics, Biology and Medicine 4/2023

14-12-2022 | Magnetic Resonance Imaging | Review

Quantitative MRI of diffuse liver diseases: techniques and tissue-mimicking phantoms

Authors: Aaryani Tipirneni-Sajja, Sarah Brasher, Utsav Shrestha, Hayden Johnson, Cara Morin, Sanjaya K. Satapathy

Published in: Magnetic Resonance Materials in Physics, Biology and Medicine | Issue 4/2023

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Abstract

Quantitative magnetic resonance imaging (MRI) techniques are emerging as non-invasive alternatives to biopsy for assessment of diffuse liver diseases of iron overload, steatosis and fibrosis. For testing and validating the accuracy of these techniques, phantoms are often used as stand-ins to human tissue to mimic diffuse liver pathologies. However, currently, there is no standardization in the preparation of MRI-based liver phantoms for mimicking iron overload, steatosis, fibrosis or a combination of these pathologies as various sizes and types of materials are used to mimic the same liver disease. Liver phantoms that mimic specific MR features of diffuse liver diseases observed in vivo are important for testing and calibrating new MRI techniques and for evaluating signal models to accurately quantify these features. In this study, we review the liver morphology associated with these diffuse diseases, discuss the quantitative MR techniques for assessing these liver pathologies, and comprehensively examine published liver phantom studies and discuss their benefits and limitations.
Literature
1.
Labranche R, Gilbert G, Cerny M, Vu KN, Soulieres D, Olivie D, Billiard JS, Yokoo T, Tang A (2018) Liver iron quantification with MR imaging: a primer for radiologists. Radiographics 38(2):392–412PubMedCrossRef
2.
Cho YJ, Kim WS, Choi YH, Lee SB, Lee S, Cheon JE, Paek M, Woo S (2020) Validation and feasibility of liver T1 mapping using free breathing MOLLI sequence in children and young adults. Sci Rep 10(1):18390PubMedPubMedCentralCrossRef
3.
Deng J, Fishbein MH, Rigsby CK, Zhang G, Schoeneman SE, Donaldson JS (2014) Quantitative MRI for hepatic fat fraction and T2* measurement in pediatric patients with non-alcoholic fatty liver disease. Pediatr Radiol 44(11):1379–1387PubMedCrossRef
4.
5.
Lazo M, Hernaez R, Eberhardt MS, Bonekamp S, Kamel I, Guallar E, Koteish A, Brancati FL, Clark JM (2013) Prevalence of nonalcoholic fatty liver disease in the United States: the Third National Health and Nutrition Examination Survey, 1988–1994. Am J Epidemiol 178(1):38–45PubMedPubMedCentralCrossRef
6.
Nelson JE, Klintworth H, Kowdley KV (2012) Iron metabolism in nonalcoholic fatty liver disease. Curr Gastroenterol Rep 14(1):8–16PubMedCrossRef
7.
Janiszewski PM, Oeffinger KC, Church TS, Dunn AL, Eshelman DA, Victor RG, Brooks S, Turoff AJ, Sinclair E, Murray JC, Bashore L, Ross R (2007) Abdominal obesity, liver fat, and muscle composition in survivors of childhood acute lymphoblastic leukemia. J Clin Endocrinol Metab 92(10):3816–3821PubMedCrossRef
8.
Yokoo T, Browning JD (2014) Fat and iron quantification in the liver: past, present, and future. Top Magn Reson Imaging 23(2):73–94PubMedCrossRef
9.
Harris R, Harman DJ, Card TR, Aithal GP, Guha IN (2017) Prevalence of clinically significant liver disease within the general population, as defined by non-invasive markers of liver fibrosis: a systematic review. Lancet Gastroenterol Hepatol 2(4):288–297PubMedCrossRef
10.
Hankins JS, McCarville MB, Loeffler RB, Smeltzer MP, Onciu M, Hoffer FA, Li CS, Wang WC, Ware RE, Hillenbrand CM (2009) R2* magnetic resonance imaging of the liver in patients with iron overload. Blood 113(20):4853–4855PubMedPubMedCentralCrossRef
11.
St Pierre TG, El-Beshlawy A, Elalfy M, Al Jefri A, Al Zir K, Daar S, Habr D, Kriemler-Krahn U, Taher A (2014) Multicenter validation of spin-density projection-assisted R2-MRI for the noninvasive measurement of liver iron concentration. Magn Reson Med 71(6):2215–2223PubMedCrossRef
12.
Zhao R, Hernando D, Harris DT, Hinshaw LA, Li K, Ananthakrishnan L, Bashir MR, Duan X, Ghasabeh MA, Kamel IR, Lowry C, Mahesh M, Marin D, Miller J, Pickhardt PJ, Shaffer J, Yokoo T, Brittain JH, Reeder SB (2021) Multisite multivendor validation of a quantitative MRI and CT compatible fat phantom. Med Phys 48(8):4375–4386PubMedCrossRef
13.
Hernando D, Sharma SD, AliyariGhasabeh M, Alvis BD, Arora SS, Hamilton G, Pan L, Shaffer JM, Sofue K, Szeverenyi NM, Welch EB, Yuan Q, Bashir MR, Kamel IR, Rice MJ, Sirlin CB, Yokoo T, Reeder SB (2017) Multisite, multivendor validation of the accuracy and reproducibility of proton-density fat-fraction quantification at 1.5T and 3T using a fat-water phantom. Magn Reson Med 77(4):1516–1524PubMedCrossRef
14.
Pickhardt PJ, Graffy PM, Reeder SB, Hernando D, Li K (2018) Quantification of liver fat content with unenhanced MDCT: phantom and clinical correlation with MRI proton density fat fraction. AJR Am J Roentgenol 211(3):W151–W157PubMedPubMedCentralCrossRef
15.
Hu HH, Yokoo T, Bashir MR, Sirlin CB, Hernando D, Malyarenko D, Chenevert TL, Smith MA, Serai SD, Middleton MS, Henderson WC, Hamilton G, Shaffer J, Shu Y, Tkach JA, Trout AT, Obuchowski N, Brittain JH, Jackson EF, Reeder SB, Committee RQIBAPB (2021) Linearity and bias of proton density fat fraction as a quantitative imaging biomarker: a multicenter, multiplatform, multivendor phantom study. Radiology 298(3):640–651PubMedCrossRef
16.
Ehman RL (2022) Magnetic resonance elastography: from invention to standard of care. Abdom Radiol (NY) 47(9):3028–3036PubMedCrossRef
17.
Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, Huang W, Noworolski SM, Young RJ, Shiroishi MS, Kim H, Coolens C, Laue H, Chung C, Rosen M, Boss M, Jackson EF (2019) Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 49(7):e101–e121PubMedCrossRef
18.
19.
20.
Krafft AJ, Loeffler RB, Song R, Bian X, McCarville MB, Hankins JS, Hillenbrand CM (2016) Does fat suppression via chemically selective saturation affect R2*-MRI for transfusional iron overload assessment? A clinical evaluation at 1.5T and 3T. Magn Reson Med 76(2):591–601PubMedCrossRef
21.
Hong W, He Q, Fan S, Carl M, Shao H, Chen J, Chang EY, Du J (2017) Imaging and quantification of iron-oxide nanoparticles (IONP) using MP-RAGE and UTE based sequences. Magn Reson Med 78(1):226–232PubMedCrossRef
22.
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 30(5):1215–1222PubMedPubMedCentralCrossRef
23.
Oudry J, Chen J, Glaser KJ, Miette V, Sandrin L, Ehman RL (2009) Cross-validation of magnetic resonance elastography and ultrasound-based transient elastography: a preliminary phantom study. J Magn Reson Imaging 30(5):1145–1150PubMedPubMedCentralCrossRef
24.
Jiang K, Ferguson CM, Ebrahimi B, Tang H, Kline TL, Burningham TA, Mishra PK, Grande JP, Macura SI, Lerman LO (2017) Noninvasive assessment of renal fibrosis with magnetization transfer MR imaging: validation and evaluation in murine renal artery stenosis. Radiology 283(1):77–86PubMedCrossRef
25.
Alustiza JM, Emparanza JI, Castiella A, Casado A, Garrido A, Aldazabal P, San Vicente M, Garcia N, Asensio AB, Banales J, Salvador E, Moyua A, Arozena X, Zarco M, Jauregui L, Vicente O (2015) Measurement of liver iron concentration by MRI is reproducible. Biomed Res Int 2015:294024PubMedPubMedCentralCrossRef
26.
Morisaka H, Motosugi U, Glaser KJ, Ichikawa S, Ehman RL, Sano K, Ichikawa T, Onishi H (2017) Comparison of diagnostic accuracies of two- and three-dimensional MR elastography of the liver. J Magn Reson Imaging 45(4):1163–1170PubMedCrossRef
27.
Reeder SB, Robson PM, Yu H, Shimakawa A, Hines CD, McKenzie CA, Brittain JH (2009) Quantification of hepatic steatosis with MRI: the effects of accurate fat spectral modeling. J Magn Reson Imaging 29(6):1332–1339PubMedPubMedCentralCrossRef
28.
29.
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(5):1620–1632PubMedPubMedCentralCrossRef
30.
Zhao R, Hamilton G, Brittain JH, Reeder SB, Hernando D (2021) Design and evaluation of quantitative MRI phantoms to mimic the simultaneous presence of fat, iron, and fibrosis in the liver. Magn Reson Med 85(2):734–747PubMedCrossRef
31.
Ahmad MS, Makhamrah O, Suardi N, Shukri A, Razak NNANA, Mohammad H (2021) Agarose and wax tissue-mimicking phantom for dynamic magnetic resonance imaging of the liver. J Med Clin Res Rev 5(12):11CrossRef
32.
In E, Naguib H, Haider M (2014) Mechanical stability analysis of carrageenan-based polymer gel for magnetic resonance imaging liver phantom with lesion particles. J Med Imaging (Bellingham) 1(3):035502PubMedCrossRef
33.
Keenan KE, Ainslie M, Barker AJ, Boss MA, Cecil KM, Charles C, Chenevert TL, Clarke L, Evelhoch JL, Finn P, Gembris D, Gunter JL, Hill DLG, Jack CR Jr, Jackson EF, Liu G, Russek SE, Sharma SD, Steckner M, Stupic KF, Trzasko JD, Yuan C, Zheng J (2018) Quantitative magnetic resonance imaging phantoms: a review and the need for a system phantom. Magn Reson Med 79(1):48–61PubMedCrossRef
34.
Stupic KF, Ainslie M, Boss MA, Charles C, Dienstfrey AM, Evelhoch JL, Finn P, Gimbutas Z, Gunter JL, Hill DLG, Jack CR, Jackson EF, Karaulanov T, Keenan KE, Liu G, Martin MN, Prasad PV, Rentz NS, Yuan C, Russek SE (2021) A standard system phantom for magnetic resonance imaging. Magn Reson Med 86(3):1194–1211PubMedPubMedCentralCrossRef
35.
Kato H, Kuroda M, Yoshimura K, Yoshida A, Hanamoto K, Kawasaki S, Shibuya K, Kanazawa S (2005) Composition of MRI phantom equivalent to human tissues. Med Phys 32(10):3199–3208PubMedCrossRef
36.
Hoffman DH, Ayoola A, Nickel D, Han F, Chandarana H, Shanbhogue KP (2020) T1 mapping, T2 mapping and MR elastography of the liver for detection and staging of liver fibrosis. Abdom Radiol (NY) 45(3):692–700PubMedCrossRef
37.
Obmann VC, Mertineit N, Marx C, Berzigotti A, Ebner L, Heverhagen JT, Christe A, Huber AT (2019) Liver MR relaxometry at 3T—segmental normal T1 and T2* values in patients without focal or diffuse liver disease and in patients with increased liver fat and elevated liver stiffness. Sci Rep 9(1):8106PubMedPubMedCentralCrossRef
38.
Ahmad MS, Suardi N, Shukri A, Mohammad H, Oglat AA, Alarab A, Makhamrah O (2020) Chemical characteristics, motivation and strategies in choice of materials used as liver phantom: a literature review. J Med Ultrasound 28(1):7–16PubMedPubMedCentral
39.
Mathur-De Vre R, Grimee R, Parmentier F, Binet J (1985) The use of agar gel as a basic reference material for calibrating relaxation times and imaging parameters. Magn Reson Med 2(2):176–179PubMedCrossRef
40.
Yoshimura K, Kato H, Kuroda M, Yoshida A, Hanamoto K, Tanaka A, Tsunoda M, Kanazawa S, Shibuya K, Kawasaki S, Hiraki Y (2003) Development of a tissue-equivalent MRI phantom using carrageenan gel. Magn Reson Med 50(5):1011–1017PubMedCrossRef
41.
Mobini N, Malekzadeh M, Haghighatkhah H, Saligheh Rad H (2020) A hybrid (iron-fat-water) phantom for liver iron overload quantification in the presence of contaminating fat using magnetic resonance imaging. MAGMA 33(3):385–392PubMedCrossRef
42.
Szurowska E, Sikorska K, Izycka-Swieszewska E, Nowicki T, Romanowski T, Bielawski KP, Studniarek M (2010) The role of MR imaging in detection of hepatic iron overload in patients with cirrhosis of different origins. BMC Gastroenterol 10:13PubMedPubMedCentralCrossRef
43.
Chang JS, Taouli B, Salibi N, Hecht EM, Chin DG, Lee VS (2006) Opposed-phase MRI for fat quantification in fat-water phantoms with 1H MR spectroscopy to resolve ambiguity of fat or water dominance. AJR Am J Roentgenol 187(1):W103-106PubMedCrossRef
44.
45.
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(5):1003–1021PubMedPubMedCentralCrossRef
46.
Pietrangelo A (2004) Hereditary hemochromatosis—a new look at an old disease. N Engl J Med 350(23):2383–2397PubMedCrossRef
47.
Franca M, Alberich-Bayarri A, Marti-Bonmati L, Oliveira P, Costa FE, Porto G, Vizcaino JR, Gonzalez JS, Ribeiro E, Oliveira J, Pessegueiro Miranda H (2017) Accurate simultaneous quantification of liver steatosis and iron overload in diffuse liver diseases with MRI. Abdom Radiol (NY) 42(5):1434–1443PubMedCrossRef
48.
Berdoukas V, Bohane T, Tobias V, De Silva K, Fraser I, Aessopos A, Lindeman R (2005) Liver iron concentration and fibrosis in a cohort of transfusion-dependent patients on long-term desferrioxamine therapy. Hematol J 5(7):572–578PubMedCrossRef
49.
Brown K, Subramony C, May W, Megason G, Liu H, Bishop P, Walker T, Nowicki MJ (2009) Hepatic iron overload in children with sickle cell anemia on chronic transfusion therapy. J Pediatr Hematol Oncol 31(5):309–312PubMedCrossRef
50.
Risdon RA, Barry M, Flynn DM (1975) Transfusional iron overload: the relationship between tissue iron concentration and hepatic fibrosis in thalassaemia. J Pathol 116(2):83–95PubMedCrossRef
51.
Thakerngpol K, Fucharoen S, Boonyaphipat P, Srisook K, Sahaphong S, Vathanophas V, Stitnimankarn T (1996) Liver injury due to iron overload in thalassemia: histopathologic and ultrastructural studies. Biometals 9(2):177–183PubMedCrossRef
52.
Chmieliauskas S, Banionis D, Laima S, Andriuskeviciute G, Mazeikiene S, Stasiuniene J, Jasulaitis A, Jarmalaite S (2017) Autopsy relevance determining hemochromatosis: case report. Medicine (Baltimore) 96(49):e8788PubMedCrossRef
53.
Iancu TC, Deugnier Y, Halliday JW, Powell LW, Brissot P (1997) Ultrastructural sequences during liver iron overload in genetic hemochromatosis. J Hepatol 27(4):628–638PubMedCrossRef
54.
55.
Guindi M (2011) Hemochromatosis. In: Saxena R (ed) Practical hepatic pathology: a diagnostic approach, W.B. Saunders, pp 177–189
56.
Ghugre NR (2008) Calibration of iron-mediated MRI relaxation by Monte Carlo modeling. Dissertation, University of Southern California
57.
Brittenham GM, Badman DG (2003) Noninvasive measurement of iron: report of an NIDDK workshop. Blood J Am Soc Hematol 101(1):15–19
58.
Wortmann AC, Froehlich PE, Pinto RB, Magalhães RB, Alvares-da-Silva MR, Ferreira JJ, Silveira TR (2007) Hepatic iron quantification by atomic absorption spectrophotometry: full validation of an analytical methodusing a fast sample preparation. Spectroscopy 21:161–167CrossRef
59.
Henninger B (2018) Demystifying liver iron concentration measurements with MRI. Eur Radiol 28(6):2535–2536PubMedCrossRef
60.
St Pierre TG, Clark PR, Chua-anusorn W, Fleming AJ, Jeffrey GP, Olynyk JK, Pootrakul P, Robins E, Lindeman R (2005) Noninvasive measurement and imaging of liver iron concentrations using proton magnetic resonance. Blood 105(2):855–861PubMedCrossRef
61.
Alexopoulou E, Stripeli F, Baras P, Seimenis I, Kattamis A, Ladis V, Efstathopoulos E, Brountzos EN, Kelekis AD, Kelekis NL (2006) R2 relaxometry with MRI for the quantification of tissue iron overload in beta-thalassemic patients. J Magn Reson Imaging 23(2):163–170PubMedCrossRef
62.
Bonny JM, Zanca M, Boire JY, Veyre A (1996) T2 maximum likelihood estimation from multiple spin-echo magnitude images. Magn Reson Med 36(2):287–293PubMedCrossRef
63.
Voskaridou E, Douskou M, Terpos E, Papassotiriou I, Stamoulakatou A, Ourailidis A, Loutradi A, Loukopoulos D (2004) Magnetic resonance imaging in the evaluation of iron overload in patients with beta thalassaemia and sickle cell disease. Br J Haematol 126(5):736–742PubMedCrossRef
64.
Chandarana H, Lim RP, Jensen JH, Hajdu CH, Losada M, Babb JS, Huffman S, Taouli B (2009) Hepatic iron deposition in patients with liver disease: preliminary experience with breath-hold multiecho T2*-weighted sequence. AJR Am J Roentgenol 193(5):1261–1267PubMedCrossRef
65.
Westwood M, Anderson LJ, Firmin DN, Gatehouse PD, Charrier CC, Wonke B, Pennell DJ (2003) A single breath-hold multiecho T2* cardiovascular magnetic resonance technique for diagnosis of myocardial iron overload. J Magn Reson Imaging 18(1):33–39PubMedCrossRef
66.
Wood JC, Enriquez C, Ghugre N, Tyzka JM, Carson S, Nelson MD, Coates TD (2005) MRI R2 and R2* mapping accurately estimates hepatic iron concentration in transfusion-dependent thalassemia and sickle cell disease patients. Blood 106(4):1460–1465PubMedPubMedCentralCrossRef
67.
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 15 and 3 Tesla. Magn Reson Med 78(5):1839–1851PubMedPubMedCentralCrossRef
68.
Tipirneni-Sajja A, Loeffler RB, Krafft AJ, Sajewski AN, Ogg RJ, Hankins JS, Hillenbrand CM (2019) Ultrashort echo time imaging for quantification of hepatic iron overload: comparison of acquisition and fitting methods via simulations, phantoms, and in vivo data. J Magn Reson Imaging 49(5):1475–1488PubMedCrossRef
69.
Doyle EK, Toy K, Valdez B, Chia JM, Coates T, Wood JC (2018) Ultra-short echo time images quantify high liver iron. Magn Reson Med 79(3):1579–1585PubMedCrossRef
70.
Sharma P, Altbach M, Galons JP, Kalb B, Martin DR (2014) Measurement of liver fat fraction and iron with MRI and MR spectroscopy techniques. Diagn Interv Radiol 20(1):17–26PubMed
71.
Taylor BA, Loeffler RB, Song R, McCarville MB, Hankins JS, Hillenbrand CM (2012) Simultaneous field and R2 mapping to quantify liver iron content using autoregressive moving average modeling. J Magn Reson Imaging 35(5):1125–1132PubMedCrossRef
72.
Wang Y (2012) Principles of magnetic resonance imaging: physics concepts, pulse sequences, and biomedical applications. CreateSpace Independent Publishing Platform, Scotts valley
73.
Liu C, Li W, Tong KA, Yeom KW, Kuzminski S (2015) Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. J Magn Reson Imaging 42(1):23–41PubMedCrossRef
74.
Dong J, Liu T, Chen F, Zhou D, Dimov A, Raj A, Cheng Q, Spincemaille P, Wang Y (2015) Simultaneous phase unwrapping and removal of chemical shift (SPURS) using graph cuts: application in quantitative susceptibility mapping. IEEE Trans Med Imaging 34(2):531–540PubMedCrossRef
75.
Liu T, Khalidov I, de Rochefort L, Spincemaille P, Liu J, Tsiouris AJ, Wang Y (2011) A novel background field removal method for MRI using projection onto dipole fields (PDF). NMR Biomed 24(9):1129–1136PubMedPubMedCentralCrossRef
76.
Liu T, Liu J, de Rochefort L, Spincemaille P, Khalidov I, Ledoux JR, Wang Y (2011) Morphology enabled dipole inversion (MEDI) from a single-angle acquisition: comparison with COSMOS in human brain imaging. Magn Reson Med 66(3):777–783PubMedCrossRef
77.
Tipirneni-Sajja A, Loeffler RB, Hankins JS, Morin C, Hillenbrand CM (2021) Quantitative susceptibility mapping using a multispectral autoregressive moving average model to assess hepatic iron overload. J Magn Reson Imaging 54(3):721–727PubMedPubMedCentralCrossRef
78.
Sharma SD, Hernando D, Horng DE, Reeder SB (2015) Quantitative susceptibility mapping in the abdomen as an imaging biomarker of hepatic iron overload. Magn Reson Med 74(3):673–683PubMedCrossRef
79.
Sharma SD, Fischer R, Schoennagel BP, Nielsen P, Kooijman H, Yamamura J, Adam G, Bannas P, Hernando D, Reeder SB (2017) MRI-based quantitative susceptibility mapping (QSM) and R2* mapping of liver iron overload: comparison with SQUID-based biomagnetic liver susceptometry. Magn Reson Med 78(1):264–270PubMedCrossRef
80.
Lin H, Wei H, He N, Fu C, Cheng S, Shen J, Wang B, Yan X, Liu C, Yan F (2018) Quantitative susceptibility mapping in combination with water-fat separation for simultaneous liver iron and fat fraction quantification. Eur Radiol 28(8):3494–3504PubMedPubMedCentralCrossRef
81.
Li J, Lin H, Liu T, Zhang Z, Prince MR, Gillen K, Yan X, Song Q, Hua T, Zhao X, Zhang M, Zhao Y, Li G, Tang G, Yang G, Brittenham GM, Wang Y (2018) Quantitative susceptibility mapping (QSM) minimizes interference from cellular pathology in R2* estimation of liver iron concentration. J Magn Reson Imaging 48(4):1069–1079PubMedPubMedCentralCrossRef
82.
Zhu A, Colgan TJ, Reeder SB, Hernando D (2018) Test–retest repeatability of R2* mapping and quantitative susceptibility mapping for liver iron quantification. In: Joint Annual Meeting ISMRM-ESMRMB, Paris
83.
Birkl C, Birkl-Toeglhofer AM, Kames C, Goessler W, Haybaeck J, Fazekas F, Ropele S, Rauscher A (2020) The influence of iron oxidation state on quantitative MRI parameters in post mortem human brain. Neuroimage 220:117080PubMedCrossRef
84.
Baldock C, Harris PJ, Piercy AR, Healy B (2001) Experimental determination of the diffusion coefficient in two-dimensions in ferrous sulphate gels using the finite element method. Aust Phys Eng Sci Med 24(1):19–30CrossRef
85.
Ibrahim EH, Khalifa AM, Eldaly AK (2016) MRI T2* imaging for assessment of liver iron overload: study of different data analysis approaches. Acta Radiol 57(12):1453–1459PubMedCrossRef
86.
Nath S, Kaittanis C, Ramachandran V, Dalal N, Perez JM (2009) Synthesis, magnetic characterization and sensing applications of novel dextran-coated iron oxide nanorods. Chem Mater 21(8):1761–1767PubMedPubMedCentralCrossRef
87.
Predescu AM, Matei E, Berbecaru AC, Pantilimon C, Dragan C, Vidu R, Predescu C, Kuncser V (2018) Synthesis and characterization of dextran-coated iron oxide nanoparticles. R Soc Open Sci 5(3):171525PubMedPubMedCentralCrossRef
88.
Lu X, Ma Y, Chang EY, He Q, Searleman A, von Drygalski A, Du J (2018) Simultaneous quantitative susceptibility mapping (QSM) and R2* for high iron concentration quantification with 3D ultrashort echo time sequences: An echo dependence study. Magn Reson Med 79(4):2315–2322PubMedPubMedCentralCrossRef
89.
Lee SS, Lee Y, Kim N, Kim SW, Byun JH, Park SH, Lee MG, Ha HK (2011) Hepatic fat quantification using chemical shift MR imaging and MR spectroscopy in the presence of hepatic iron deposition: validation in phantoms and in patients with chronic liver disease. J Magn Reson Imaging 33(6):1390–1398PubMedCrossRef
90.
Brown GC, Cowin GJ, Galloway GJ (2017) A USPIO doped gel phantom for R2* relaxometry. MAGMA 30(1):15–27PubMedCrossRef
91.
Wang YX (2011) Superparamagnetic iron oxide based MRI contrast agents: current status of clinical application. Quant Imaging Med Surg 1(1):35–40PubMedPubMedCentral
92.
Chandarana H, Do RK, Mussi TC, Jensen JH, Hajdu CH, Babb JS, Taouli B (2012) The effect of liver iron deposition on hepatic apparent diffusion coefficient values in cirrhosis. AJR Am J Roentgenol 199(4):803–808PubMedCrossRef
93.
Xiao YD, Paudel R, Liu J, Ma C, Zhang ZS, Zhou SK (2016) MRI contrast agents: classification and application (Review). Int J Mol Med 38(5):1319–1326PubMedCrossRef
94.
95.
Yokoo T, Yuan Q, Senegas 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):1544–1559PubMedCrossRef
96.
Kee Y, Sandino CM, Syed AB, Cheng JY, Shimakawa A, Colgan TJ, Hernando D, Vasanawala SS (2021) Free-breathing R2* mapping of hepatic iron overload in children using 3D multi-echo UTE cones MRI. Magn Reson Med 85(5):2608–2621PubMedPubMedCentralCrossRef
97.
Younossi Z, Tacke F, Arrese M, Chander Sharma B, Mostafa I, Bugianesi E, Wai-Sun Wong V, Yilmaz Y, George J, Fan J (2019) Global perspectives on nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatology 69(6):2672–2682PubMedCrossRef
98.
Bellentani S, Saccoccio G, Masutti F, Crocè LS, Brandi G, Sasso F, Cristanini G, Tiribelli C (2000) Prevalence of and risk factors for hepatic steatosis in Northern Italy. Ann Intern Med 132(2):112–117PubMedCrossRef
99.
Browning JD, Szczepaniak LS, Dobbins R, Nuremberg P, Horton JD, Cohen JC, Grundy SM, Hobbs HH (2004) Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatology 40(6):1387–1395PubMedCrossRef
100.
101.
102.
Lai J, Wang HL, Zhang X, Wang H, Liu X (2022) Pathologic diagnosis of nonalcoholic fatty liver disease. Arch Pathol Lab Med 146(8):940–946PubMedCrossRef
103.
Tanikawa K (1968) Ultrastructural aspects of the liver and its disorders. Igaku Shoin Ltd, Tokyo
104.
Kleiner DE, Brunt EM, Van Natta M, Behling C, Contos MJ, Cummings OW, Ferrell LD, Liu YC, Torbenson MS, Unalp-Arida A (2005) Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 41(6):1313–1321PubMedCrossRef
105.
Szczepaniak LS, Nurenberg P, Leonard D, Browning JD, Reingold JS, Grundy S, Hobbs HH, Dobbins RL (2005) Magnetic resonance spectroscopy to measure hepatic triglyceride content: prevalence of hepatic steatosis in the general population. Am J Physiol Endocrinol Metab 288(2):E462-468PubMedCrossRef
106.
Caussy C, Reeder SB, Sirlin CB, Loomba R (2018) Noninvasive, quantitative assessment of liver fat by MRI-PDFF as an endpoint in NASH trials. Hepatology 68(2):763–772PubMedCrossRef
107.
Hernando D, Sharma SD, Kramer H, Reeder SB (2014) On the confounding effect of temperature on chemical shift-encoded fat quantification. Magn Reson Med 72(2):464–470PubMedCrossRef
108.
Navaratna R, Zhao R, Colgan TJ, Hu HH, Bydder M, Yokoo T, Bashir MR, Middleton MS, Serai SD, Malyarenko D, Chenevert T, Smith M, Henderson W, Hamilton G, Shu Y, Sirlin CB, Tkach JA, Trout AT, Brittain JH, Hernando D, Reeder SB, Committee RQIBA-PDFFB (2021) Temperature-corrected proton density fat fraction estimation using chemical shift-encoded MRI in phantoms. Magn Reson Med 86(1):69–81PubMedPubMedCentralCrossRef
109.
Tognarelli JM, Dawood M, Shariff MI, Grover VP, Crossey MM, Cox IJ, Taylor-Robinson SD, McPhail MJ (2015) Magnetic resonance spectroscopy: principles and techniques: lessons for clinicians. J Clin Exp Hepatol 5(4):320–328PubMedPubMedCentralCrossRef
110.
Bydder M, Girard O, Hamilton G (2011) Mapping the double bonds in triglycerides. Magn Reson Imaging 29(8):1041–1046PubMedCrossRef
111.
Hamilton G, Yokoo T, Bydder M, Cruite I, Schroeder ME, Sirlin CB, Middleton MS (2011) In vivo characterization of the liver fat (1)H MR spectrum. NMR Biomed 24(7):784–790PubMedCrossRef
112.
Kurhanewicz J, Vigneron DB, Nelson SJ (2000) Three-dimensional magnetic resonance spectroscopic imaging of brain and prostate cancer. Neoplasia 2(1–2):166–189PubMedPubMedCentralCrossRef
113.
Cassidy FH, Yokoo T, Aganovic L, Hanna RF, Bydder M, Middleton MS, Hamilton G, Chavez AD, Schwimmer JB, Sirlin CB (2009) Fatty liver disease: MR imaging techniques for the detection and quantification of liver steatosis. Radiographics 29(1):231–260PubMedCrossRef
114.
Omoumi P (2022) The Dixon method in musculoskeletal MRI: from fat-sensitive to fat-specific imaging. Skeletal Radiol 51(7):1365–1369PubMedCrossRef
115.
Hayashi T, Saitoh S, Takahashi J, Tsuji Y, Ikeda K, Kobayashi M, Kawamura Y, Fujii T, Inoue M, Miyati T, Kumada H (2017) Hepatic fat quantification using the two-point Dixon method and fat color maps based on non-alcoholic fatty liver disease activity score. Hepatol Res 47(5):455–464PubMedCrossRef
116.
Clarke CN, Choi H, Hou P, Davis CH, Ma J, Rashid A, Vauthey JN, Aloia TA (2017) Using MRI to non-invasively and accurately quantify preoperative hepatic steatosis. HPB (Oxford) 19(8):706–712PubMedCrossRef
117.
Pacifico L, Martino MD, Catalano C, Panebianco V, Bezzi M, Anania C, Chiesa C (2011) T1-weighted dual-echo MRI for fat quantification in pediatric nonalcoholic fatty liver disease. World J Gastroenterol 17(25):3012–3019PubMedPubMedCentralCrossRef
118.
Kim G, Giannini C, Pierpont B, Feldstein AE, Santoro N, Kursawe R, Shaw M, Duran E, Goldberg R, Dziura J, Caprio S (2013) Longitudinal effects of MRI-measured hepatic steatosis on biomarkers of glucose homeostasis and hepatic apoptosis in obese youth. Diabetes Care 36(1):130–136PubMedCrossRef
119.
Lins CF, Salmon CEG, Nogueira-Barbosa MH (2021) Applications of the Dixon technique in the evaluation of the musculoskeletal system. Radiol Bras 54(1):33–42PubMedPubMedCentralCrossRef
120.
121.
Bhat V, Velandai S, Belliappa V, Illayraja J, Halli KG, Gopalakrishnan G (2017) Quantification of liver fat with mDIXON magnetic resonance imaging, comparison with the computed tomography and the biopsy. J Clin Diagn Res 11(7):TC06-TC10PubMedPubMedCentral
122.
Reeder SB, Sirlin CB (2010) Quantification of liver fat with magnetic resonance imaging. Magn Reson Imaging Clin 18(3):337–357CrossRef
123.
Kuhn JP, Jahn C, Hernando D, Siegmund W, Hadlich S, Mayerle J, Pfannmoller J, Langner S, Reeder S (2014) T1 bias in chemical shift-encoded liver fat-fraction: role of the flip angle. J Magn Reson Imaging 40(4):875–883PubMedCrossRef
124.
Wang X, Colgan TJ, Hinshaw LA, Roberts NT, Bancroft LCH, Hamilton G, Hernando D, Reeder SB (2020) T1 -corrected quantitative chemical shift-encoded MRI. Magn Reson Med 83(6):2051–2063PubMedCrossRef
125.
Eggers H, Bornert P (2014) Chemical shift encoding-based water-fat separation methods. J Magn Reson Imaging 40(2):251–268PubMedCrossRef
126.
Reeder SB, Cruite I, Hamilton G, Sirlin CB (2011) Quantitative assessment of liver fat with magnetic resonance imaging and spectroscopy. J Magn Reson Imaging 34(4):729–749PubMedPubMedCentralCrossRef
127.
Henninger B, Zoller H, Kannengiesser S, Zhong X, Jaschke W, Kremser C (2017) 3D multiecho dixon for the evaluation of hepatic iron and fat in a clinical setting. J Magn Reson Imaging 46(3):793–800PubMedCrossRef
128.
Kuhn JP, Hernando D, Mensel B, Kruger PC, Ittermann T, Mayerle J, Hosten N, Reeder SB (2014) Quantitative chemical shift-encoded MRI is an accurate method to quantify hepatic steatosis. J Magn Reson Imaging 39(6):1494–1501PubMedCrossRef
129.
Yokoo T, Shiehmorteza M, Hamilton G, Wolfson T, Schroeder ME, Middleton MS, Bydder M, Gamst AC, Kono Y, Kuo A, Patton HM, Horgan S, Lavine JE, Schwimmer JB, Sirlin CB (2011) Estimation of hepatic proton-density fat fraction by using MR imaging at 3.0 T. Radiology 258(3):749–759PubMedPubMedCentralCrossRef
130.
Idilman IS, Keskin O, Celik A, Savas B, Elhan AH, Idilman R, Karcaaltincaba M (2016) A comparison of liver fat content as determined by magnetic resonance imaging-proton density fat fraction and MRS versus liver histology in non-alcoholic fatty liver disease. Acta Radiol 57(3):271–278PubMedCrossRef
131.
Tang A, Tan J, Sun M, Hamilton G, Bydder M, Wolfson T, Gamst AC, Middleton M, Brunt EM, Loomba R, Lavine JE, Schwimmer JB, Sirlin CB (2013) Nonalcoholic fatty liver disease: MR imaging of liver proton density fat fraction to assess hepatic steatosis. Radiology 267(2):422–431PubMedPubMedCentralCrossRef
132.
Tang A, Desai A, Hamilton G, Wolfson T, Gamst A, Lam J, Clark L, Hooker J, Chavez T, Ang BD, Middleton MS, Peterson M, Loomba R, Sirlin CB (2015) Accuracy of MR imaging-estimated proton density fat fraction for classification of dichotomized histologic steatosis grades in nonalcoholic fatty liver disease. Radiology 274(2):416–425PubMedCrossRef
133.
Beyer C, Hutton C, Andersson A, Imajo K, Nakajima A, Kiker D, Banerjee R, Dennis A (2021) Comparison between magnetic resonance and ultrasound-derived indicators of hepatic steatosis in a pooled NAFLD cohort. PLoS One 16(4):e0249491PubMedPubMedCentralCrossRef
134.
Chebrolu VV, Hines CD, Yu H, Pineda AR, Shimakawa A, McKenzie CA, Samsonov A, Brittain JH, Reeder SB (2010) Independent estimation of T* 2 for water and fat for improved accuracy of fat quantification. Magn Reson Med 63(4):849–857PubMedPubMedCentralCrossRef
135.
Horng DE, Hernando D, Hines CD, Reeder SB (2013) Comparison of R2* correction methods for accurate fat quantification in fatty liver. J Magn Reson Imaging 37(2):414–422PubMedCrossRef
136.
Hernando D, Kramer JH, Reeder SB (2013) Multipeak fat-corrected complex R2* relaxometry: theory, optimization, and clinical validation. Magn Reson Med 70(5):1319–1331PubMedPubMedCentralCrossRef
137.
Taylor BA, Hwang KP, Hazle JD, Stafford RJ (2009) Autoregressive moving average modeling for spectral parameter estimation from a multigradient echo chemical shift acquisition. Med Phys 36(3):753–764PubMedPubMedCentralCrossRef
138.
Krafft AJ, Taylor BA, Lin H, Loeffler RB, Hillenbrand CM (2013) A systematic evaluation of an auto regressive moving average (ARMA) model for fat-water quantification and simultaneous T2* mapping. In: International Society of Magnetic Resonance in Medicine, Salt Lake City, Utah
139.
Pooler BD, Hernando D, Ruby JA, Ishii H, Shimakawa A, Reeder SB (2018) Validation of a motion-robust 2D sequential technique for quantification of hepatic proton density fat fraction during free breathing. J Magn Reson Imaging 48(6):1578–1585PubMedPubMedCentralCrossRef
140.
Jaubert O, Cruz G, Bustin A, Schneider T, Lavin B, Koken P, Hajhosseiny R, Doneva M, Rueckert D, Botnar RM, Prieto C (2020) Water-fat Dixon cardiac magnetic resonance fingerprinting. Magn Reson Med 83(6):2107–2123PubMedCrossRef
141.
Schneider E, Remer EM, Obuchowski NA, McKenzie CA, Ding X, Navaneethan SD (2021) Long-term inter-platform reproducibility, bias, and linearity of commercial PDFF MRI methods for fat quantification: a multi-center, multi-vendor phantom study. Eur Radiol 31(10):7566–7574PubMedCrossRef
142.
Bernard CP, Liney GP, Manton DJ, Turnbull LW, Langton CM (2008) Comparison of fat quantification methods: a phantom study at 3.0T. J Magn Reson Imaging 27(1):192–197PubMedCrossRef
143.
Peng XG, Ju S, Qin Y, Fang F, Cui X, Liu G, Ni Y, Teng GJ (2011) Quantification of liver fat in mice: comparing dual-echo Dixon imaging, chemical shift imaging, and 1H-MR spectroscopy. J Lipid Res 52(10):1847–1855PubMedCrossRef
144.
Leporq B, Lambert SA, Ronot M, Vilgrain V, Van Beers BE (2014) Quantification of the triglyceride fatty acid composition with 3.0 T MRI. NMR Biomed 27(10):1211–1221PubMedCrossRef
145.
Fukuzawa K, Hayashi T, Takahashi J, Yoshihara C, Tano M, Kotoku J, 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):349–358PubMedCrossRef
146.
Hayashi T, Fukuzawa K, Yamazaki H, Konno T, Miyati T, Kotoku J, Oba H, Kondo H, Toyoda K, Saitoh S (2018) Multicenter, multivendor phantom study to validate proton density fat fraction and T2* values calculated using vendor-provided 6-point DIXON methods. Clin Imaging 51:38–42PubMedCrossRef
147.
Kim HJ, Cho HJ, Kim B, You MW, Lee JH, Huh J, Kim JK (2019) Accuracy and precision of proton density fat fraction measurement across field strengths and scan intervals: a phantom and human study. J Magn Reson Imaging 50(1):305–314PubMedCrossRef
148.
Mashhood A, Railkar R, Yokoo T, Levin Y, Clark L, Fox-Bosetti S, Middleton MS, Riek J, Kauh E, Dardzinski BJ, Williams D, Sirlin C, Shire NJ (2013) Reproducibility of hepatic fat fraction measurement by magnetic resonance imaging. J Magn Reson Imaging 37(6):1359–1370PubMedCrossRef
149.
Water in Mineral Oil in Water (W–O–W) Double Emulsion Production using SDS, PGPR and Tween® 80 as Emulsifiers (2019). Dolomite Microfluidics
150.
151.
Zdrali E, Etienne G, Smolentsev N, Amstad E, Roke S (2019) The interfacial structure of nano- and micron-sized oil and water droplets stabilized with SDS and Span80. J Chem Phys 150(20):204704PubMedCrossRef
152.
Wang Q, Ye F, Ma P, Chen F, Che Y, Zhao X, Yang L (2019) Quantitative magnetic resonance imaging evaluation of hepatic fat content with iron deposition: will it be disturbed? J Int Med Res 47(5):1958–1974PubMedPubMedCentralCrossRef
153.
Fritz V, Martirosian P, Machann J, Daniels R, Schick F (2022) A comparison of emulsifiers for the formation of oil-in-water emulsions: stability of the emulsions within 9 h after production and MR signal properties. MAGMA 35(3):401–410PubMedCrossRef
154.
Nahmias Y, Berthiaume F, Yarmush ML (2007) Integration of technologies for hepatic tissue engineering. Adv Biochem Eng Biotechnol 103:309–329PubMed
155.
Khurana A, Sayed N, Allawadhi P, Weiskirchen R (2021) It’s all about the spaces between cells: role of extracellular matrix in liver fibrosis. Ann Transl Med 9(8):728PubMedPubMedCentralCrossRef
156.
Karsdal MA, Nielsen SH, Leeming DJ, Langholm LL, Nielsen MJ, Manon-Jensen T, Siebuhr A, Gudmann NS, Ronnow S, Sand JM, Daniels SJ, Mortensen JH, Schuppan D (2017) The good and the bad collagens of fibrosis—their role in signaling and organ function. Adv Drug Deliv Rev 121:43–56PubMedCrossRef
157.
158.
Chen G, Xia B, Fu Q, Huang X, Wang F, Chen Z, Lv Y (2019) Matrix mechanics as regulatory factors and therapeutic targets in hepatic fibrosis. Int J Biol Sci 15(12):2509–2521PubMedPubMedCentralCrossRef
159.
Bazrafshan Z, Stylios GK (2019) Spinnability of collagen as a biomimetic material: a review. Int J Biol Macromol 129:693–705PubMedCrossRef
160.
Civan JM (2019) Hepatic Fibrosis. merckmanuals.com/professional/hepatic-and-biliary-disorders/fibrosis-and-cirrhosis/hepatic-fibrosis.
161.
Milic S, Mikolasevic I, Orlic L, Devcic E, Starcevic-Cizmarevic N, Stimac D, Kapovic M, Ristic S (2016) The role of iron and iron overload in chronic liver disease. Med Sci Monit 22:2144–2151PubMedPubMedCentralCrossRef
162.
Puri P, Sanyal AJ (2012) Nonalcoholic fatty liver disease: definitions, risk factors, and workup. Clin Liver Dis (Hoboken) 1(4):99–103PubMedCrossRef
163.
Idilman IS, Li J, Yin M, Venkatesh SK (2020) MR elastography of liver: current status and future perspectives. Abdom Radiol (NY) 45(11):3444–3462PubMedCrossRef
164.
Guglielmo FF, Venkatesh SK, Mitchell DG (2019) Liver MR elastography technique and image interpretation: pearls and pitfalls. Radiographics 39(7):1983–2002PubMedCrossRef
165.
Venkatesh SK, Yin M, Ehman RL (2013) Magnetic resonance elastography of liver: technique, analysis, and clinical applications. J Magn Reson Imaging 37(3):544–555PubMedPubMedCentralCrossRef
166.
167.
Wang Y, Ganger DR, Levitsky J, Sternick LA, McCarthy RJ, Chen ZE, Fasanati CW, Bolster B, Shah S, Zuehlsdorff S, Omary RA, Ehman RL, Miller FH (2011) Assessment of chronic hepatitis and fibrosis: comparison of MR elastography and diffusion-weighted imaging. AJR Am J Roentgenol 196(3):553–561PubMedPubMedCentralCrossRef
168.
Serai SD, Obuchowski NA, Venkatesh SK, Sirlin CB, Miller FH, Ashton E, Cole PE, Ehman RL (2017) Repeatability of MR elastography of liver: a meta-analysis. Radiology 285(1):92–100PubMedCrossRef
169.
Ozturk A, Olson MC, Samir AE, Venkatesh SK (2022) Liver fibrosis assessment: MR and US elastography. Abdom Radiol (NY) 47(9):3037–3050PubMedCrossRef
170.
Tang A, Cloutier G, Szeverenyi NM, Sirlin CB (2015) Ultrasound elastography and MR elastography for assessing liver fibrosis: part 2, diagnostic performance, confounders, and future directions. AJR Am J Roentgenol 205(1):33–40PubMedPubMedCentralCrossRef
171.
Yin M, Talwalkar JA, Glaser KJ, Manduca A, Grimm RC, Rossman PJ, Fidler JL, Ehman RL (2007) Assessment of hepatic fibrosis with magnetic resonance elastography. Clin Gastroenterol Hepatol 5(10):1207–1213PubMedPubMedCentralCrossRef
172.
Akkaya HE, Erden A, Kuru Oz D, Unal S, Erden I (2018) Magnetic resonance elastography: basic principles, technique, and clinical applications in the liver. Diagn Interv Radiol 24(6):328–335PubMedPubMedCentralCrossRef
173.
174.
175.
Banerjee R, Pavlides M, Tunnicliffe EM, Piechnik SK, Sarania N, Philips R, Collier JD, Booth JC, Schneider JE, Wang LM, Delaney DW, Fleming KA, Robson MD, Barnes E, Neubauer S (2014) Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease. J Hepatol 60(1):69–77PubMedPubMedCentralCrossRef
176.
Tanwar S, Rhodes F, Srivastava A, Trembling PM, Rosenberg WM (2020) Inflammation and fibrosis in chronic liver diseases including non-alcoholic fatty liver disease and hepatitis C. World J Gastroenterol 26(2):109–133PubMedPubMedCentralCrossRef
177.
Li Z, Sun J, Hu X, Huang N, Han G, Chen L, Zhou Y, Bai W, Yang X (2016) Assessment of liver fibrosis by variable flip angle T1 mapping at 30T. J Magn Reson Imaging 43(3):698–703PubMedCrossRef
178.
Haaf P, Garg P, Messroghli DR, Broadbent DA, Greenwood JP, Plein S (2016) Cardiac T1 mapping and extracellular volume (ECV) in clinical practice: a comprehensive review. J Cardiovasc Magn Reson 18(1):89PubMedPubMedCentralCrossRef
179.
Ding Y, Rao SX, Zhu T, Chen CZ, Li RC, Zeng MS (2015) Liver fibrosis staging using T1 mapping on gadoxetic acid-enhanced MRI compared with DW imaging. Clin Radiol 70(10):1096–1103PubMedCrossRef
180.
Heye T, Yang SR, Bock M, Brost S, Weigand K, Longerich T, Kauczor HU, Hosch W (2012) MR relaxometry of the liver: significant elevation of T1 relaxation time in patients with liver cirrhosis. Eur Radiol 22(6):1224–1232PubMedCrossRef
181.
Haimerl M, Utpatel K, Verloh N, Zeman F, Fellner C, Nickel D, Teufel A, Fichtner-Feigl S, Evert M, Stroszczynski C, Wiggermann P (2017) Gd-EOB-DTPA-enhanced MR relaxometry for the detection and staging of liver fibrosis. Sci Rep 7:41429PubMedPubMedCentralCrossRef
182.
Sheng RF, Wang HQ, Yang L, Jin KP, Xie YH, Fu CX, Zeng MS (2017) Assessment of liver fibrosis using T1 mapping on Gd-EOB-DTPA-enhanced magnetic resonance. Dig Liver Dis 49(7):789–795PubMedCrossRef
183.
Elsafty HG, El Shafey M, El Arabawy R, Mahrous MR, Dawoud TM (2021) Could native T1 mapping replace late gadolinium enhancement in the assessment of myocardial fibrosis in patients with cardiomyopathy? Egypt J Radiol Nucl Med 52(1):222CrossRef
184.
Mojtahed A, Kelly CJ, Herlihy AH, Kin S, Wilman HR, McKay A, Kelly M, Milanesi M, Neubauer S, Thomas EL, Bell JD, Banerjee R, Harisinghani M (2019) Reference range of liver corrected T1 values in a population at low risk for fatty liver disease-a UK Biobank sub-study, with an appendix of interesting cases. Abdom Radiol (NY) 44(1):72–84PubMedCrossRef
185.
Hoad CL, Palaniyappan N, Kaye P, Chernova Y, James MW, Costigan C, Austin A, Marciani L, Gowland PA, Guha IN, Francis ST, Aithal GP (2015) A study of T(1) relaxation time as a measure of liver fibrosis and the influence of confounding histological factors. NMR Biomed 28(6):706–714PubMedCrossRef
186.
Obmann VC, Berzigotti A, Catucci D, Ebner L, Grani C, Heverhagen JT, Christe A, Huber AT (2021) T1 mapping of the liver and the spleen in patients with liver fibrosis-does normalization to the blood pool increase the predictive value? Eur Radiol 31(6):4308–4318PubMedCrossRef
187.
Faria SC, Ganesan K, Mwangi I, Shiehmorteza M, Viamonte B, Mazhar S, Peterson M, Kono Y, Santillan C, Casola G, Sirlin CB (2009) MR imaging of liver fibrosis: current state of the art. Radiographics 29(6):1615–1635PubMedCrossRef
188.
Talwalkar JA, Yin M, Fidler JL, Sanderson SO, Kamath PS, Ehman RL (2008) Magnetic resonance imaging of hepatic fibrosis: emerging clinical applications. Hepatology 47(1):332–342PubMedCrossRef
189.
Girometti R, Furlan A, Esposito G, Bazzocchi M, Como G, Soldano F, Isola M, Toniutto P, Zuiani C (2008) Relevance of b-values in evaluating liver fibrosis: a study in healthy and cirrhotic subjects using two single-shot spin-echo echo-planar diffusion-weighted sequences. J Magn Reson Imaging 28(2):411–419PubMedCrossRef
190.
Zhu J, Zhang J, Gao JY, Li JN, Yang DW, Chen M, Zhou C, Yang ZH (2017) Apparent diffusion coefficient normalization of normal liver: will it improve the reproducibility of diffusion-weighted imaging at different MR scanners as a new biomarker? Medicine (Baltimore) 96(3):e5910PubMedCrossRef
191.
Mostafa MA, Kamal O, Yassin A, Nagi MA, Ahmed OA, Ahmed HA (2020) The diagnostic value of normalized ADC using spleen as reference organ in assessment liver fibrosis. Egypt J Radiol Nucl Med 51(1):112CrossRef
192.
Shin MK, Song JS, Hwang SB, Hwang HP, Kim YJ, Moon WS (2019) Liver fibrosis assessment with diffusion-weighted imaging: value of liver apparent diffusion coefficient normalization using the spleen as a reference organ. Diagnostics (Basel) 9(3):207
193.
El-Hariri M, Ali TFT, Hussien HIM (2013) Apparent diffusion coefficient (ADC) in liver fibrosis: Usefulness of normalized ADC using the spleen as reference organ. Egypt J Radiol Nucl Med 44(3):441–451CrossRef
194.
Petitclerc L, Sebastiani G, Gilbert G, Cloutier G, Tang A (2017) Liver fibrosis: review of current imaging and MRI quantification techniques. J Magn Reson Imaging 45(5):1276–1295PubMedCrossRef
195.
Leitao HS, Doblas S, d’Assignies G, Garteiser P, Daire JL, Paradis V, Geraldes CF, Vilgrain V, Van Beers BE (2013) Fat deposition decreases diffusion parameters at MRI: a study in phantoms and patients with liver steatosis. Eur Radiol 23(2):461–467PubMedCrossRef
196.
Liu CH, Liang CC, Huang KW, Liu CJ, Chen SI, Lin JW, Hung PH, Tsai HB, Lai MY, Chen PJ, Chen JH, Chen DS, Kao JH (2011) Transient elastography to assess hepatic fibrosis in hemodialysis chronic hepatitis C patients. Clin J Am Soc Nephrol 6(5):1057–1065PubMedPubMedCentralCrossRef
197.
Yin M, Woollard J, Wang X, Torres VE, Harris PC, Ward CJ, Glaser KJ, Manduca A, Ehman RL (2007) Quantitative assessment of hepatic fibrosis in an animal model with magnetic resonance elastography. Magn Reson Med 58(2):346–353PubMedCrossRef
198.
199.
Rojas GS, Dies P, Tobón SH (2019) Stiffness of liver-mimicking phantom for magnetic resonance elastography. In: Proceedings of the 15th scientific meeting, Mexican Symposium on Medical Physics, Mexico City, Mexico, p 040007
200.
Andoh F, Yue JL, Julea F, Tardieu M, Nous C, Page G, Garteiser P, Van Beers BE, Maitre X, Pellot-Barakat C (2021) Multifrequency magnetic resonance elastography for elasticity quantitation and optimal tissue discrimination: a two-platform liver fibrosis mimicking phantom study. NMR Biomed 34(8):e4543PubMedCrossRef
201.
Salameh N, Sarracanie M, Armstrong BD, Rosen MS, Comment A (2016) Overhauser-enhanced magnetic resonance elastography. NMR Biomed 29(5):607–613PubMedCrossRef
202.
Kishimoto R, Suga M, Koyama A, Omatsu T, Tachibana Y, Ebner DK, Obata T (2017) Measuring shear-wave speed with point shear-wave elastography and MR elastography: a phantom study. BMJ Open 7(1):e013925PubMedPubMedCentralCrossRef
203.
Usumura M, Kishimoto R, Ishii K, Hotta E, Kershaw J, Higashi T, Obata T, Suga M (2021) Longitudinal stability of a multimodal visco-elastic polyacrylamide gel phantom for magnetic resonance and ultrasound shear-wave elastography. PLoS ONE 16(5):e0250667PubMedPubMedCentralCrossRef
204.
Kishimoto R, Suga M, Usumura M, Iijima H, Yoshida M, Hachiya H, Shiina T, Yamakawa M, Konno K, Obata T, Yamaguchi T (2022) Shear wave speed measurement bias in a viscoelastic phantom across six ultrasound elastography systems: a comparative study with transient elastography and magnetic resonance elastography. J Med Ultrason (2001) 49(2):143–152PubMedCrossRef
205.
206.
Meneses A, Santabarbara JM, Romero JA, Aliaga R, Maceira AM, Moratal D (2021) Determination of non-invasive biomarkers for the assessment of fibrosis, steatosis and hepatic iron overload by MR image analysis. a pilot study. Diagnostics (Basel) 11(7)
207.
Tirkes T, Zhao X, Lin C, Stuckey AJ, Li L, Giri S, Nickel D (2019) Evaluation of variable flip angle, MOLLI, SASHA, and IR-SNAPSHOT pulse sequences for T1 relaxometry and extracellular volume imaging of the pancreas and liver. MAGMA 32(5):559–566PubMedPubMedCentralCrossRef
208.
Statton BK, Smith J, Finnegan ME, Koerzdoerfer G, Quest RA, Grech-Sollars M (2022) Temperature dependence, accuracy, and repeatability of T1 and T2 relaxation times for the ISMRM/NIST system phantom measured using MR fingerprinting. Magn Reson Med 87(3):1446–1460PubMedCrossRef
209.
Lewis B, Guta A, Mackey S, Gach HM, Mutic S, Green O, Kim T (2021) Evaluation of diffusion-weighted MRI and geometric distortion on a 0.35T MR-LINAC at multiple gantry angles. J Appl Clin Med Phys 22(2):118–125PubMedPubMedCentralCrossRef
210.
Girometti R, Furlan A, Bazzocchi M, Soldano F, Isola M, Toniutto P, Bitetto D, Zuiani C (2007) Diffusion-weighted MRI in evaluating liver fibrosis: a feasibility study in cirrhotic patients. Radiol Med 112(3):394–408PubMedCrossRef
211.
Cui Y, Dyvorne H, Besa C, Cooper N, Taouli B (2015) IVIM Diffusion-weighted Imaging of the Liver at 3.0T: Comparison with 1.5T. Eur J Radiol Open 2:123–128PubMedPubMedCentralCrossRef
212.
Sharma P, Martin DR, Pineda N, Xu Q, Vos M, Anania F, Hu X (2009) Quantitative analysis of T2-correction in single-voxel magnetic resonance spectroscopy of hepatic lipid fraction. J Magn Reson Imaging 29(3):629–635PubMedPubMedCentralCrossRef
213.
Colgan TJ, Zhao R, Roberts NT, Hernando D, Reeder SB (2021) Limits of fat quantification in the presence of iron overload. J Magn Reson Imaging 54(4):1166–1174PubMedPubMedCentralCrossRef
214.
Thangavel K, SaritaŞ EÜ (2017) Aqueous paramagnetic solutions for MRI phantoms at 3 T: a detailed study on relaxivities. Turk J Electr Eng Comput Sci 25:2108–2121CrossRef
215.
Wood JC, Otto-Duessel M, Aguilar M, Nick H, Nelson MD, Coates TD, Pollack H, Moats R (2005) Cardiac iron determines cardiac T2*, T2, and T1 in the gerbil model of iron cardiomyopathy. Circulation 112(4):535–543PubMedPubMedCentralCrossRef
216.
Henninger B, Kremser C, Rauch S, Eder R, Zoller H, Finkenstedt A, Michaely HJ, Schocke M (2012) Evaluation of MR imaging with T1 and T2* mapping for the determination of hepatic iron overload. Eur Radiol 22(11):2478–2486PubMedCrossRef
217.
Simchick G, Zhao R, Hamilton G, Reeder SB, Hernando D (2021) Spectroscopy-based multi-parametric quantification in subjects with liver iron overload at 1.5T and 3T. Magn Reson Med 87(2):597–613PubMedPubMedCentralCrossRef
218.
Thamizharasan G, Russell A, Beinkampen J, Holtrop J, Williams J, Tipirneni-Sajja A (2019) Magnetic resonance elastography phantoms to mimic liver tissue stiffness and validation with uniaxial compression test. In: Paper presented at the Biomedical Engineering Society Meeting
219.
Tsai YS, Chen JS, Wang CK, Lu CH, Cheng CN, Kuo CS, Liu YS, Tsai HM (2014) Quantitative assessment of iron in heart and liver phantoms using dual-energy computed tomography. Exp Ther Med 8(3):907–912PubMedPubMedCentralCrossRef
220.
Guimaraes AR, Siqueira L, Uppal R, Alford J, Fuchs BC, Yamada S, Tanabe K, Chung RT, Lauwers G, Chew ML, Boland GW, Sahani DV, Vangel M, Hahn PF, Caravan P (2016) T2 relaxation time is related to liver fibrosis severity. Quant Imaging Med Surg 6(2):103–114PubMedPubMedCentralCrossRef
221.
Headley AM, Grice JV, Pickens DR (2020) Reproducibility of liver iron concentration estimates in MRI through R2* measurement determined by least-squares curve fitting. J Appl Clin Med Phys 21(12):295–303PubMedPubMedCentralCrossRef
222.
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(4):964–971PubMedCrossRef
Metadata
Title
Quantitative MRI of diffuse liver diseases: techniques and tissue-mimicking phantoms
Authors
Aaryani Tipirneni-Sajja
Sarah Brasher
Utsav Shrestha
Hayden Johnson
Cara Morin
Sanjaya K. Satapathy
Publication date
14-12-2022
Publisher
Springer International Publishing
Published in
Magnetic Resonance Materials in Physics, Biology and Medicine / Issue 4/2023
Print ISSN: 0968-5243
Electronic ISSN: 1352-8661
DOI
https://doi.org/10.1007/s10334-022-01053-z

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