Comparison of post processing methods between Java Magnetic Resonance User Interface (jMRUI) and Totally Automatic Robust Quantitation in NMR (TARQUIN) software for liver fat quantification

Main Article Content

Duanghathai Pasanta
Montree Tungjai
Sirirat Chancharunee
Suchart Kiatwattanacharoen
Suchart Kothan


Background: Proton magnetic resonance spectroscopy or 1H MRS is a validated and non-invasive method used for studying liver fat. However, the metabolite spectra obtained by 1H MRS require a post-processing method for accurate liver fat quanti­fication. Various spectrum analysis software has been developed and is being used in many studies. To the best of our knowledge, no comparisons between spectrum analysis software for liver fat quantification have yet been completed.

Objectives: To compare the post processing methods between java-based graphical for MR user interface packages (jMRUI) and totally automatic robust quantitation in NMR (TARQUIN) software for optimal liver fat quantification.

Materials and methods: 1H MRS spectrum from the right lobe of the liver was obtained for post processing. Liver fat qualification was done by AMARES algorithms on jMRUI software, and automatic quantification algorithms was initiated by TARQUIN software. A total of 30 subjects participated in this study. Subjects were separated into a control group (n=15) and an overweight group (n=15) for liver fat quantification. Liver lipids at 0.9 ppm (-CH3 lipids) and 1.3 ppm (-CH2 lipids) were fitted and quantified. The results obtained from both jMRUI and TARQUIN post processing software packages for both groups were then compared.

Results: A strong and moderate correlation of signal intensity between jMRUI and TARQUIN software was found (total lipids, r=0.836, p<0.001; -CH2 lipids, r=0.848, p<0.001; -CH3 lipids, r=0.520, and p<0.003). Liver lipid levels were generally higher in the overweight group. There was a 2.35 times level of change in the overweight group compared to control from jMRUI, and there was a 2.16 times level of change in the overweight group compared to control from TARQUIN. There was no statistical differences between the programs (p=0.762).

Conclusion: Both jMRUI and TARQUIN are feasible post processing tools for 1H MRS liver spectrum fitting for liver lipids quantification.


Download data is not yet available.

Article Details

How to Cite
Pasanta, D., Tungjai, M., Chancharunee, S., Kiatwattanacharoen, S., & Kothan, S. (2018). Comparison of post processing methods between Java Magnetic Resonance User Interface (jMRUI) and Totally Automatic Robust Quantitation in NMR (TARQUIN) software for liver fat quantification. Journal of Associated Medical Sciences, 51(3), 150-156. Retrieved from
Radiologic Technology


[1] Younossi Z, Anstee QM, Marietti M, Hardy T, Henry L, Eslam M, et al. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nature reviews Gastroenterology & hepatology. 2018;15(1):11-20.

[2] Anstee QM, Targher G, Day CP. Progression of NAFLD to diabetes mellitus, cardiovascular disease or cirrhosis. Nature reviews Gastroenterology & hepatology. 2013;10(6):330-44.

[3] Fan J-G, Kim S-U, Wong VW-S. New trends on obesity and NAFLD in Asia. Journal of Hepatology. 2017;67(4):862-73.

[4] Poovorawan K, Treeprasertsuk S, Thepsuthammarat K, Wilairatana P, Kitsahawong B, Phaosawasdi K. The burden of cirrhosis and impact of universal coverage public health care system in Thailand: Nationwide study. Annals of hepatology. 2015;14(6):862-8.

[5] Cowin GJ, Jonsson JR, Bauer JD, Ash S, Ali A, Osland EJ, et al. Magnetic resonance imaging and spectroscopy for monitoring liver steatosis. Journal of magnetic resonance imaging : JMRI. 2008;28(4):937-45.

[6] Idilman IS, Keskin O, Celik A, Savas B, Elhan AH, Idilman R, et al. 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 radiologica (Stockholm, Sweden : 1987). 2016;57(3):271-8.

[7] Cassidy FH, Yokoo T, Aganovic L, Hanna RF, Bydder M, Middleton MS, et al. Fatty liver disease: MR imaging techniques for the detection and quantification of liver steatosis. Radiographics : a review publication of the Radiological Society of North America, Inc. 2009;29(1):231-60.

[8] Lallukka S, Sädevirta S, Kallio MT, Luukkonen PK, Zhou Y, Hakkarainen A, et al. Predictors of Liver Fat and Stiffness in Non-Alcoholic Fatty Liver Disease (NAFLD) – an 11-Year Prospective Study. Scientific Reports. 2017;7(1):14561.

[9] Traussnigg S, Kienbacher C, Gajdosik M, Valkovic L, Halilbasic E, Stift J, et al. Ultra-high-field magnetic resonance spectroscopy in non-alcoholic fatty liver disease: Novel mechanistic and diagnostic insights of energy metabolism in non-alcoholic steatohepatitis and advanced fibrosis. Liver international : official journal of the International Association for the Study of the Liver. 2017;37(10):1544-53.

[10] Wilson M, Reynolds G, Kauppinen RA, Arvanitis TN, Peet AC. A constrained least-squares approach to the automated quantitation of in vivo (1)H magnetic resonance spectroscopy data. Magnetic resonance in medicine. 2011;65(1):1-12.

[11] Naressi A, Couturier C, Devos JM, Janssen M, Mangeat C, de Beer R, et al. Java-based graphical user interface for the MRUI quantitation package. Magma (New York, NY). 2001;12(2-3):141-52.

[12] Dcf Stefan D, Cesare F, Andrasescu A, Popa E, Lazariev A, Vescovo E, et al. Quantitation of magnetic resonance spectroscopy signals: The jMRUI software package2009. 104035 p.

[13] Vanhamme L, van den Boogaart A, Van Huffel S. Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. Journal of magnetic resonance (San Diego, Calif : 1997). 1997;129(1):35-43.

[14] Vilar-Gomez E, Martinez-Perez Y, Calzadilla-Bertot L, Torres-Gonzalez A, Gra-Oramas B, Gonzalez-Fabian L, et al. Weight Loss Through Lifestyle Modification Significantly Reduces Features of Nonalcoholic Steatohepatitis. Gastroenterology. 2015;149(2):367-78.e5; quiz e14-5.

[15] Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organization technical report series. 1995;854:1-452.

[16] Martin SS, Blaha MJ, Elshazly MB, Toth PP, Kwiterovich PO, Blumenthal RS, et al. Comparison of a novel method vs the Friedewald equation for estimating low-density lipoprotein cholesterol levels from the standard lipid profile. Jama. 2013;310(19):2061-8.

[17] Sathiyakumar V, Park J, Golozar A, Lazo M, Quispe R, Guallar E, et al. Fasting Versus Nonfasting and Low-Density Lipoprotein Cholesterol Accuracy. Circulation. 2018;137(1):10-9.

[18] Pijnappel WWF, Boogaart A, de Beer R, van Ormondt D. Svd-Based Quantification of Magnetic-Resonance Signals1992. 122-34 p.

[19] Ouwerkerk R, Pettigrew RI, Gharib AM. Liver metabolite concentrations measured with 1H MR spectroscopy. Radiology. 2012;265(2):565-75.

[20] Vanhamme L, Fierro RD, Van Huffel S, de Beer R. Fast Removal of Residual Water in Proton Spectra. Journal of magnetic resonance (San Diego, Calif : 1997). 1998;132(2):197-203.

[21] Lallukka S, Sädevirta S, T. Kallio M, K. Luukkonen P, Zhou Y, Hakkarainen A, et al. Predictors of Liver Fat and Stiffness in Non-Alcoholic Fatty Liver Disease (NAFLD) – an 11-Year Prospective Study2017.

[22] Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology (Baltimore, Md). 2016;64(1):73-84.

[23] Haas JT, Francque S, Staels B. Pathophysiology and Mechanisms of Nonalcoholic Fatty Liver Disease. Annu Rev Physiol. 2016;78:181-205.

[24] Szczepaniak LS, Nurenberg P, Leonard D, Browning JD, Reingold JS, Grundy S, et al. Magnetic resonance spectroscopy to measure hepatic triglyceride content: prevalence of hepatic steatosis in the general population. American journal of physiology Endocrinology and metabolism. 2005;288(2):E462-8.

[25] Ruhl CE, Everhart JE. Determinants of the association of overweight with elevated serum alanine aminotransferase activity in the United States. Gastroenterology. 2003;124(1):71-9.

[26] Klop B, Elte JW, Cabezas MC. Dyslipidemia in obesity: mechanisms and potential targets. Nutrients. 2013;5(4):1218-40.

[27] Kopelman P. Health risks associated with overweight and obesity. Obesity reviews : an official journal of the International Association for the Study of Obesity. 2007;8 Suppl 1:13-7.

[28] Esteghamati A, Khalilzadeh O, Anvari M, Ahadi MS, Abbasi M, Rashidi A. Metabolic syndrome and insulin resistance significantly correlate with body mass index. Archives of medical research. 2008;39(8):803-8.

[29] Meisamy S, Hines CD, Hamilton G, Sirlin CB, McKenzie CA, Yu H, et al. Quantification of hepatic steatosis with T1-independent, T2-corrected MR imaging with spectral modeling of fat: blinded comparison with MR spectroscopy. Radiology. 2011;258(3):767-75.

[30] Huh JH, Kim KJ, Kim SU, Han SH, Han KH, Cha BS, et al. Obesity is more closely related with hepatic steatosis and fibrosis measured by transient elastography than metabolic health status. Metabolism: clinical and experimental. 2017;66:23-31.

[31] Ezzat WM, Ragab S, Ismail NA, Elhosary YA, ElBaky AMNEA, Farouk H, et al. Frequency of non-alcoholic fatty liver disease in overweight/obese children and adults: Clinical, sonographic picture and biochemical assessment. Journal of Genetic Engineering and Biotechnology. 2012;10(2):221-7.