Hepatic fat assessment using advanced Magnetic Resonance Imaging
Research Highlight

Hepatic fat assessment using advanced Magnetic Resonance Imaging

Yong Pang1, Baiying Yu2, Xiaoliang Zhang1,3,4

1Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States; 2Magwale, Palo Alto, CA, United States; 3UCSF/UC Berkeley Joint Graduate Group in Bioengineering, San Francisco & Berkeley, CA, United States; 4California Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA, United States

Corresponding to:
Xiaoliang Zhang. Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States.
Email: xlzhang@berkeley.edu.

Submitted Jul 27, 2012. Accepted for publication Aug 31, 2012.
DOI: 10.3978/j.issn.2223-4292.2012.08.05

Magnetic Resonance Imaging is capable of providing clinically-valuable images for hepatic diseases such as the fatty liver and has become a promising noninvasive method in evaluating human liver under normal and diseased conditions (1-9). Fatty liver is one of the most common abnormalities. Recent surveys have shown that it affects up to 15% of the general population and it is higher among those with obesity and high alcohol consumption (10-12). Fatty liver is commonly associated with alcohol overuse, obesity, hyperlipidemia and hepatitis, and will cause steatosis within hepatocytes (13-17), which may progress to steatohepatitis and then cirrhosis (18-20). Liver biopsy is considered the diagnostic reference standard for the assessment of fatty liver, however it is invasive and prone to complications and is no longer considered as mandatory as first line screening tools for fatty liver (21). MRI provides different contrast between the different tissues of human abdomen, and has potential to quantitatively assess the hepatic liver in patients with fatty liver and predict the degree of steatosis of liver (22-34). Some quantitative imaging methods have been proposed for evaluating the hepatic fat, such as, Dixon method (22-24,34-38), the in-phase, opposed phase gradient echo MR imaging method (25,32), and proton MR spectroscopy method. However the insufficient image resolution and long acquisition time limit its quantitative capability. In addition, the motion artifacts caused by breathing and heartbeat become a major problem in further improvement of hepatic image quality in practice. It is highly demanded to increase the imaging speed and also the image resolution, which is challenging in present liver MRI routines. Recent years, high and ultrahigh field MRI (39-50,51-64), such as 7T, has shown its inherent ability to improve signal to noise ratio in human head imaging (42-45,48,49), prostate imaging (50,65), spine imaging (46,54) and abdominal imaging (51,59,66). It is expected to achieve better signal to noise ratio (SNR) and thus high resolution in liver imaging. However, transferring liver imaging protocols to ultrahigh fields faces many practical difficulties and technical challenges in both RF coil design and sequence design for human liver imaging due to the pronounced radiation losses, chemical shift, motion artifacts and B1 variation (31) at high fields. There is an urgent demand for technical development for liver imaging in both MR hardware and fast acquisition strategies using ultrahigh field MR.

Recent years, the microstrip transmission line (MTL) RF coils (40,55,67-69) have shown advantages in high and ultrahigh field MR applications with high frequency operation capability, high quality factors, reduced radiation losses and improved MR sensitivity. Its unmatched decoupling feature is essential for high field RF transmit/receive array designs. An example is the flexible transceiver array developed for ultrahigh field 7 T MR applications by using the first and second order harmonics of the microstrip resonator (55,70). The mixed harmonic MTL resonator technique greatly improves the decoupling performance, reduces noise correlations between resonant elements, and enhances parallel imaging performance. This technique does not require physical connection or decoupling network between array elements, which is commonly used in conventional coil array designs for implementing decoupling. Consequently the geometry and size of the microstrip flexible array can be conveniently adjusted to best fit patients, achieving the best filling factor and therefore the increased signal to noise ratio for human liver imaging.

In fast imaging methods, parallel imaging has demonstrated the unique capability in accelerating MR imaging by using the different sensitivity profiles of RF coil array elements to replace the phase encoding. The undersampled raw data can be reconstructed using a special reconstruction method to achieve a correct image with significantly reduced aliasing (71-76). Our previously proposed flexible microstrip array can be readily utilized for parallel imaging to accelerate the hepatic imaging and thus help reduce the motion artifacts. On the other hand, parallel transmission is able to shorten the RF pulse width for spatial selective excitation by using transceiver coil arrays and the sensitivity information (77-81). Although the specific absorption ratio (SAR) grows with the acceleration rate, the SAR can be optimized using different strategies such as variable sampling rate or optimized k-space trajectories (78,82,83). In human liver imaging, the power deposition is always an important safety issue while the imaging speed is critical to imaging quality. Parallel transmission strategy thus provides effective ways to help making a tradeoff between the power deposition and imaging speed for hepatic imaging.

Recently the compressed sensing (84) MRI which can greatly reduce the raw data size required for image reconstruction and shorten the imaging time by using significantly undersampled k-space demonstrates great potential to perform fast imaging with high image quality and enhanced image resolution (85-99). This is very helpful for liver imaging because motion artifacts caused by breathing and heartbeat often deteriorate liver image quality. Unlike the parallel imaging, compressed sensing technique basically does not require any new hardware for implementation. However, at the high acceleration rate, the contrast to noise ratio (CNR) normally decreases quickly due to the use of significantly undersampled k-space data. This is not desired in liver imaging because the tissue contrast plays an important role in differentiating normal and diseased liver tissues. The interpolated compressed sensing (iCS) MR image reconstruction method proposed recently would be possible to improve CNR and even SNR at high acceleration rates for multi-slice 2D imaging applications (98). For a significantly undersampled slice some missed raw data can be estimated by using the raw data from the neighboring slice convolved by a weighting function. This strategy helps improve the CNR and also SNR of the images of multi-slice 2D MRI. It would be advantageous to apply the iCS method to hepatic imaging and develop specialized MR pulse sequence and reconstruction method to dramatically shorten the acquisition time while maintain the CNR. This would provide an efficient imaging tool for quantitatively assessing the liver fat and monitoring therapy outcome of the fatty liver non-invasively.

In summary, the advanced MRI techniques such as ultrahigh field, novel RF transceiver arrays, parallel imaging techniques, parallel transmission and compressed sensing would be advantageous in augmenting its quantitative capability and gaining better diagnosis and characterization of fatty liver diseases. To realize this and provide clinically-valuable images, dedicated RF transceivers, specific imaging sequence and reconstruction methods have to be explored and investigated to satisfy the clinical requirements.


This work was supported by NIH grants R01EB008699 and P41EB013598, and a QB3 Research Award.

Disclosure: The authors declare no conflict of interest.


  1. Hennig J, Weigel M, Scheffler K. Multiecho sequences with variable refocusing flip angles: optimization of signal behavior using smooth transitions between pseudo steady states (TRAPS). Magn Reson Med 2003;49:527-35.
  2. Lee VS, Lavelle MT, Rofsky NM, et al. Hepatic MR imaging with a dynamic contrast-enhanced isotropic volumetric interpolated breath-hold examination: feasibility, reproducibility, and technical quality. Radiology 2000;215:365-72.
  3. McFarland EG, Mayo-Smith WW, Saini S, et al. Hepatic hemangiomas and malignant tumors: improved differentiation with heavily T2-weighted conventional spin-echo MR imaging. Radiology 1994;193:43-7.
  4. Merkle EM, Dale BM, Paulson EK. Abdominal MR imaging at 3T. Magn Reson Imaging Clin N Am 2006;14:17-26.
  5. Mitchell DG, Saini S, Weinreb J, et al. Hepatic metastases and cavernous hemangiomas: distinction with standard- and triple-dose gadoteridol-enhanced MR imaging. Radiology 1994;193:49-57.
  6. Mortelé KJ, Praet M, Van Vlierberghe H, et al. CT and MR imaging findings in focal nodular hyperplasia of the liver: radiologic-pathologic correlation. AJR Am J Roentgenol 2000;175:687-92.
  7. Ramalho M, Altun E, Heredia E, et al. Liver MR imaging: 1.5T versus 3T. Magn Reson Imaging Clin N Am 2007;15:321-47, vi.
  8. Smith FW, Mallard JR, Reid A, et al. Nuclear magnetic resonance tomographic imaging in liver disease. Lancet 1981;1:963-6.
  9. Zech CJ, Herrmann KA, Huber A, et al. High-resolution MR-imaging of the liver with T2-weighted sequences using integrated parallel imaging: comparison of prospective motion correction and respiratory triggering. J Magn Reson Imaging 2004;20:443-50.
  10. Bellentani S, Saccoccio G, Masutti F, et al. Prevalence of and risk factors for hepatic steatosis in Northern Italy. Ann Intern Med 2000;132:112-7.
  11. Kammen BF, Pacharn P, Thoeni RF, et al. Focal fatty infiltration of the liver: analysis of prevalence and CT findings in children and young adults. AJR Am J Roentgenol 2001;177:1035-9.
  12. Luyckx FH, Desaive C, Thiry A, et al. Liver abnormalities in severely obese subjects: effect of drastic weight loss after gastroplasty. Int J Obes Relat Metab Disord 1998;22:222-6.
  13. Allard JP. Other disease associations with non-alcoholic fatty liver disease (NAFLD). Best Pract Res Clin Gastroenterol 2002;16:783-95.
  14. Angulo P. Nonalcoholic fatty liver disease. N Engl J Med 2002;346:1221-31.
  15. Brunt EM, Tiniakos DG. Pathology of steatohepatitis. Best Pract Res Clin Gastroenterol 2002;16:691-707.
  16. Clark JM, Diehl AM. Nonalcoholic fatty liver disease: an underrecognized cause of cryptogenic cirrhosis. JAMA 2003;289:3000-4.
  17. Eaton S, Record CO, Bartlett K. Multiple biochemical effects in the pathogenesis of alcoholic fatty liver. Eur J Clin Invest 1997;27:719-22.
  18. Lefkowitch JH. Morphology of alcoholic liver disease. Clin Liver Dis 2005;9:37-53.
  19. Méndez-Sánchez N, Almeda-Valdés P, Uribe M. Alcoholic liver disease. An update. Ann Hepatol 2005;4:32-42.
  20. Wanless IR, Shiota K. The pathogenesis of nonalcoholic steatohepatitis and other fatty liver diseases: a four-step model including the role of lipid release and hepatic venular obstruction in the progression to cirrhosis. Semin Liver Dis 2004;24:99-106.
  21. Pais R, Lupşor M, Poantă L, et al. Liver biopsy versus noninvasive methods--fibroscan and fibrotest in the diagnosis of non-alcoholic fatty liver disease: a review of the literature. Rom J Intern Med 2009;47:331-40.
  22. Bashir MR, Dale BM, Merkle EM, et al. Automated liver sampling using a gradient dual-echo Dixon-based technique. Magn Reson Med 2012;67:1469-77.
  23. Peng XG, Ju S, Qin Y, Fang F, et al. Quantification of liver fat in mice: comparing dual-echo Dixon imaging, chemical shift imaging, and 1H-MR spectroscopy. J Lipid Res 2011;52:1847-55.
  24. Rosenkrantz AB, Mannelli L, Kim S, et al. Gadolinium-enhanced liver magnetic resonance imaging using a 2-point Dixon fat-water separation technique: impact upon image quality and lesion detection. J Comput Assist Tomogr 2011;35:96-101.
  25. Fishbein M, Castro F, Cheruku S, et al. Hepatic MRI for fat quantitation: its relationship to fat morphology, diagnosis, and ultrasound. J Clin Gastroenterol 2005;39:619-25.
  26. Hamer OW, Aguirre DA, Casola G, et al. Fatty liver: imaging patterns and pitfalls. Radiographics 2006;26:1637-53.
  27. Hussain HK, Chenevert TL, Londy FJ, et al. Hepatic fat fraction: MR imaging for quantitative measurement and display--early experience. Radiology 2005;237:1048-55.
  28. Kreft BP, Tanimoto A, Baba Y, et al. Diagnosis of fatty liver with MR imaging. J Magn Reson Imaging 1992;2:463-71.
  29. Marks SJ, Moore NR, Ryley NG, et al. Measurement of liver fat by MRI and its reduction by dexfenfluramine in NIDDM. Int J Obes Relat Metab Disord 1997;21:274-9.
  30. Martín J, Puig J, Falcó J, et al. Hyperechoic liver nodules: characterization with proton fat-water chemical shift MR imaging. Radiology 1998;207:325-30.
  31. Padormo F, Malik S, Hajnal J, et al. Assessing and Correcting Respiration Induced Variation of B1 in the Liver. Proceedings 17th Scientific Meeting, International Society for Magnetic Resonance in Medicine 2009:753.
  32. Rinella ME, McCarthy R, Thakrar K, et al. Dual-echo, chemical shift gradient-echo magnetic resonance imaging to quantify hepatic steatosis: Implications for living liver donation. Liver Transpl 2003;9:851-6.
  33. Venkataraman S, Braga L, Semelka RC. Imaging the fatty liver. Magn Reson Imaging Clin N Am 2002;10:93-103.
  34. Zhang X, Tengowski M, Fasulo L, et al. Measurement of fat/water ratios in rat liver using 3D three-point dixon MRI. Magn Reson Med 2004;51:697-702.
  35. Glover GH. Multipoint Dixon technique for water and fat proton and susceptibility imaging. J Magn Reson Imaging 1991;1:521-30.
  36. Glover GH, Schneider E. Three-point Dixon technique for true water/fat decomposition with B0 inhomogeneity correction. Magn Reson Med 1991;18:371-83.
  37. Dixon WT. Simple proton spectroscopic imaging. Radiology 1984;153:189-94.
  38. Schertz LD, Lee JK, Heiken JP, et al. Proton spectroscopic imaging (Dixon method) of the liver: clinical utility. Radiology 1989;173:401-5.
  39. Vaughan JT, Garwood M, Collins CM, et al. 7T vs. 4T: RF power, homogeneity, and signal-to-noise comparison in head images. Magn Reson Med 2001;46:24-30.
  40. Zhang X, Ugurbil K, Chen W. Microstrip RF surface coil design for extremely high-field MRI and spectroscopy. Magn Reson Med 2001;46:443-50.
  41. Zhang X, Ugurbil K, Chen W. A microstrip transmission line volume coil for human head MR imaging at 4T. J Magn Reson 2003;161:242-51.
  42. Avdievich NI, Hetherington HP, Kuznetsov AM, et al. 7T head volume coils: improvements for rostral brain imaging. J Magn Reson Imaging 2009;29:461-5.
  43. Choi C, Dimitrov IE, Douglas D, et al. Improvement of resolution for brain coupled metabolites by optimized (1)H MRS at 7T. NMR Biomed 2010;23:1044-52.
  44. Eapen M, Zald DH, Gatenby JC, et al. Using high-resolution MR imaging at 7T to evaluate the anatomy of the midbrain dopaminergic system. AJNR Am J Neuroradiol 2011;32:688-94.
  45. Fukunaga M, Li TQ, van Gelderen P, et al. Layer-specific variation of iron content in cerebral cortex as a source of MRI contrast. Proc Natl Acad Sci U S A 2010;107:3834-9.
  46. Kraff O, Bitz AK, Kruszona S, et al. An eight-channel phased array RF coil for spine MR imaging at 7 T. Invest Radiol 2009;44:734-40.
  47. Lei H, Zhu XH, Zhang XL, et al. In vivo 31P magnetic resonance spectroscopy of human brain at 7 T: an initial experience. Magn Reson Med 2003;49:199-205.
  48. Li TQ, Yao B, van Gelderen P, et al. Characterization of T(2)* heterogeneity in human brain white matter. Magn Reson Med 2009;62:1652-7.
  49. Liu S, Gonen O, Fleysher L, et al. Regional metabolite T2 in the healthy rhesus macaque brain at 7T. Magn Reson Med 2008;59:1165-9.
  50. Metzger GJ, Snyder C, Akgun C, et al. Local B1+ shimming for prostate imaging with transceiver arrays at 7T based on subject-dependent transmit phase measurements. Magn Reson Med 2008;59:396-409.
  51. Pang Y, Wu B, Wang C, et al. “7T human liver imaging using microstrip surface coil.” in Proc 18th Annual Meeting ISMRM, Stockholm, 2010:2587.
  52. Pang Y, Xie Z, Li Y, et al. Resonant Mode Reduction in Radiofrequency Volume Coils for Ultrahigh Field Magnetic Resonance Imaging. Materials (Basel) 2011;4:1333-4.
  53. Wiggins GC, Potthast A, Triantafyllou C, et al. Eight-channel phased array coil and detunable TEM volume coil for 7 T brain imaging. Magn Reson Med 2005;54:235-40.
  54. Wu B, Wang C, Krug R, et al. 7T human spine imaging arrays with adjustable inductive decoupling. IEEE Trans Biomed Eng 2010;57:397-403.
  55. Wu B, Wang C, Lu J, et al. Multi-channel microstrip transceiver arrays using harmonics for high field MR imaging in humans. IEEE Trans Med Imaging 2012;31:183-91.
  56. Yacoub E, Van De Moortele PF, Shmuel A, et al. Signal and noise characteristics of Hahn SE and GE BOLD fMRI at 7 T in humans. Neuroimage 2005;24:738-50.
  57. Zelinski AC, Angelone LM, Goyal VK, et al. Specific absorption rate studies of the parallel transmission of inner-volume excitations at 7T. J Magn Reson Imaging 2008;28:1005-18.
  58. Yankeelov TE, DeBusk LM, Billheimer DD, et al. Repeatability of a reference region model for analysis of murine DCE-MRI data at 7T. J Magn Reson Imaging 2006;24:1140-7.
  59. Pang Y, Wu B, Wang C, et al. Numerical Analysis of Human Sample Effect on RF Penetration and Liver MR Imaging at Ultrahigh Field. Concepts Magn Reson Part B Magn Reson Eng 2011;39B:206-16.
  60. Zhang X, Ugurbil K, Sainati R, et al. An inverted-microstrip resonator for human head proton MR imaging at 7 tesla. IEEE Trans Biomed Eng 2005;52:495-504.
  61. Collins CM, Liu W, Swift BJ, et al. Combination of optimized transmit arrays and some receive array reconstruction methods can yield homogeneous images at very high frequencies. Magn Reson Med 2005;54:1327-32.
  62. Collins CM, Wang Z, Mao W, et al. Array-optimized composite pulse for excellent whole-brain homogeneity in high-field MRI. Magn Reson Med 2007;57:470-4.
  63. Qian Y, Zhao T, Hue YK, et al. High-resolution spiral imaging on a whole-body 7T scanner with minimized image blurring. Magn Reson Med 2010;63:543-52.
  64. Gilbert KM, Belliveau JG, Curtis AT, et al. A conformal transceive array for 7 T neuroimaging. Magn Reson Med 2012;67:1487-96.
  65. van den Bergen B, Klomp DW, Raaijmakers AJ, et al. Uniform prostate imaging and spectroscopy at 7 T: comparison between a microstrip array and an endorectal coil. NMR Biomed 2010. NMR Biomed 2011;24:358-65.
  66. Vaughan JT, Snyder CJ, DelaBarre LJ, et al. Whole-body imaging at 7T: preliminary results. Magn Reson Med 2009;61:244-8.
  67. Lee RF, Hardy CJ, Sodickson DK, et al. Lumped-element planar strip array (LPSA) for parallel MRI. Magn Reson Med 2004;51:172-83.
  68. Adriany G, Van de Moortele PF, Wiesinger F, et al. Transmit and receive transmission line arrays for 7 Tesla parallel imaging. Magn Reson Med 2005;53:434-45.
  69. Zhang X, Zhu XH, Chen W. Higher-order harmonic transmission-line RF coil design for MR applications. Magn Reson Med 2005;53:1234-9.
  70. Wu B, Zhang X, Wang C, et al. Flexible transceiver array for ultrahigh field human MR imaging. Magn Reson Med 2012;68:1332-8.
  71. Sodickson DK, Manning WJ. Simultaneous acquisition of spatial harmonics (SMASH): fast imaging with radiofrequency coil arrays. Magn Reson Med 1997;38:591-603.
  72. Pruessmann KP, Weiger M, Scheidegger MB, et al. SENSE: sensitivity encoding for fast MRI. Magn Reson Med 1999;42:952-62.
  73. Heidemann RM, Griswold MA, Haase A, et al. VD-AUTO-SMASH imaging. Magn Reson Med 2001;45:1066-74.
  74. Jakob PM, Griswold MA, Edelman RR, et al. AUTO-SMASH: a self-calibrating technique for SMASH imaging. SiMultaneous Acquisition of Spatial Harmonics. MAGMA 1998;7:42-54.
  75. Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 2002;47:1202-10.
  76. Pang Y, Vigneron DB, Zhang X. Parallel traveling-wave MRI: a feasibility study. Magn Reson Med 2012;67:965-78.
  77. Katscher U, Börnert P, Leussler C, et al. Transmit SENSE. Magn Reson Med 2003;49:144-50.
  78. Zhu Y. Parallel excitation with an array of transmit coils. Magn Reson Med 2004;51:775-84.
  79. Grissom W, Yip CY, Zhang Z, et al. Spatial domain method for the design of RF pulses in multicoil parallel excitation. Magn Reson Med 2006;56:620-9.
  80. Ma C, Xu D, King KF, et al. Joint design of spoke trajectories and RF pulses for parallel excitation. Magn Reson Med 2011;65:973-85.
  81. Pang Y, Zhang X. Precompensation for mutual coupling between array elements in parallel excitation. Quant Imaging Med Surg 2011;1:4-10.
  82. Homann H, Graesslin I, Nehrke K, et al. Specific absorption rate reduction in parallel transmission by k-space adaptive radiofrequency pulse design. Magn Reson Med 2011;65:350-7.
  83. Liu Y, Ji JX. Minimal-SAR RF pulse optimization for parallel transmission in MRI. Conf Proc IEEE Eng Med Biol Soc 2008;2008:5774-7.
  84. Donoho DL. Compressed sensing. IEEE Trans Inform Theory 2006;52:1289-1306.
  85. Lustig M, Donoho D, Pauly JM. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med 2007;58:1182-95.
  86. Doneva M, Börnert P, Eggers H, et al. Compressed sensing for chemical shift-based water-fat separation. Magn Reson Med 2010;64:1749-59.
  87. Holland DJ, Malioutov DM, Blake A, et al. Reducing data acquisition times in phase-encoded velocity imaging using compressed sensing. J Magn Reson 2010;203:236-46.
  88. Hong M, Yu Y, Wang H, et al. Compressed sensing MRI with singular value decomposition-based sparsity basis. Phys Med Biol 2011;56:6311-25.
  89. Ji JX, Zhao C, Lang T. Compressed sensing parallel magnetic resonance imaging. Conf Proc IEEE Eng Med Biol Soc 2008;2008:1671-4.
  90. Kim D, Dyvorne HA, Otazo R, et al. Accelerated phase-contrast cine MRI using k-t SPARSE-SENSE. Magn Reson Med 2012;67:1054-64.
  91. Li W, Griswold M, Yu X. “Fast cardiac T(1) mapping in mice using a model-based compressed sensing method,” Magn Reson Med 2012;68:1127-34.
  92. Liang D, Liu B, Wang J, et al. Accelerating SENSE using compressed sensing. Magn Reson Med 2009;62:1574-84.
  93. Otazo R, Kim D, Axel L, et al. Combination of compressed sensing and parallel imaging for highly accelerated first-pass cardiac perfusion MRI. Magn Reson Med 2010;64:767-76.
  94. Hu S, Lustig M, Balakrishnan A, et al. 3D compressed sensing for highly accelerated hyperpolarized (13)C MRSI with in vivo applications to transgenic mouse models of cancer. Magn Reson Med 2010;63:312-21.
  95. Larson PE, Hu S, Lustig M, et al. Fast dynamic 3D MR spectroscopic imaging with compressed sensing and multiband excitation pulses for hyperpolarized 13C studies. Magn Reson Med 2011;65:610-9.
  96. Cunningham CH, Chen AP, Albers MJ, et al. Double spin-echo sequence for rapid spectroscopic imaging of hyperpolarized 13C. J Magn Reson 2007;187:357-62.
  97. Haldar JP, Hernando D, Liang ZP. Compressed-sensing MRI with random encoding. IEEE Trans Med Imaging 2011;30:893-903.
  98. Pang Y, Zhang X. Interpolated Compressed Sensing MR Image Reconstruction using Neighboring Slice k-space Data. Proceeding in International Society of Magnetic Resonance in Medicine 2012:2275.
  99. Wu B, Li W, Guidon A, et al. Whole brain susceptibility mapping using compressed sensing. Magn Reson Med 2012;67:137-47.
Cite this article as: Pang Y, Yu B, Zhang X. Hepatic fat assessment using advanced Magnetic Resonance Imaging. Quant Imaging Med Surg 2012;2(3):213-218. DOI: 10.3978/ j.issn.2223-4292.2012.08.05