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Magnetic Resonance imaging analysis of liver fibrosis and inflammation: overwhelming gray zones restrict clinical use

  • Special Section: Diffuse Liver Disease
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Abstract

Magnetic resonance (MR) identification and grading of subjects with liver fibrosis and inflammation represents a clinical challenge. MR elastography plays a well-defined role in fibrosis estimation, but its use is not widely available in clinical settings. Given that liver MR is becoming the reference standard for fat and iron quantitation, there is a need to clarify whether there is any role for MR imaging in the concomitant evaluation of fibrosis and inflammation in this setting. This review summarizes the diagnostic estimations of different MR imaging parameters obtained from conventional non-contrast-enhanced multiple b values diffusion-weighted acquisitions, variable flip angles T1 relaxation maps and STIR images. Although some derived parameters have shown a significant correlation to histological scores, a small magnitude of effect with wide overlap across severity grades is the rule. Contrary to fat and iron quantification, the low precision and reproducibility of MR imaging metrics limits its clinical relevance in fibrosis and inflammation assessment. In a sequential clinical approach combining different methodologies, MR imaging has no applicability for ruling-out and low accuracy for ruling-in advanced fibrosis. Thereby, MR elastography remains as the only image method with high diagnostic accuracy for the detection of advanced fibrosis. Until date, inflammation remains in a gray zone where biopsy cannot be replaced, and further investigations are needed. The present review offers an in-depth discuss of the MR imaging diagnostic performance for the evaluation of liver fibrosis and inflammation, highlighting the need for scientific improvements.

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Abbreviations

A:

Inflammation grade

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

CLD:

Chronic liver diseases

D:

True molecular diffusion coefficient of water

D*:

Pseudo-diffusion coefficient related to the incoherent microcirculation

DDC:

Distributed diffusion coefficient

DW:

Diffusion weighted

f :

Percentage of microvascular volume or perfusion fraction

F:

Fibrosis stage

L/F:

Signal intensity of liver to subcutaneous fat ratio

IVIM-DW:

Intravoxel incoherent motion diffusion-weighted image

MR:

Magnetic resonance

NAFLD:

Non-alcoholic fatty liver disease

NASH:

Non-alcoholic steatohepatitis

NPV:

Negative predictive value

PPV:

Positive predictive value

STE-DW:

Stretched-exponential model diffusion-weighted image

STIR:

Short-inversion time inversion recovery

T1-VFA:

3D-T1-weighted spoiled gradient echo with variable flip angles

α:

Intravoxel heterogeneity index

References

  1. Pimpin L, Cortez-Pinto H, Negro F, et al; EASL HEPAHEALTH Steering Committee. (2018) Burden of liver disease in Europe: Epidemiology and analysis of risk factors to identify prevention policies. J Hepatol. 69: 718-735.

    Article  PubMed  Google Scholar 

  2. Asrani SK, Kouznetsova M, Ogola G, et al. (2018) Increasing Health Care Burden of Chronic Liver Disease Compared with Other Chronic Diseases, 2004–2013. Gastroenterology. 155: 719–729.

    Article  PubMed  Google Scholar 

  3. Ratziu V, Charlotte F, Heurtier A, et al; LIDO Study Group. (2005) Sampling Variability of Liver Biopsy in Nonalcoholic Fatty Liver Disease. Gastroenterology. 128: 1898–1906.

    Article  PubMed  Google Scholar 

  4. Ishak K, Baptista A, Bianchi L, et al. (1995) Histological grading and staging of chronic hepatitis. J Hepatol. 22: 696–699.

    Article  CAS  PubMed  Google Scholar 

  5. Bedossa P, Carrat F. (2009) Liver biopsy: The best, not the gold standard. J Hepatol. 50(1): 1–3.

    Article  PubMed  Google Scholar 

  6. Campos CF, Paiva DD, Perazzo H, et al. (2014) An inexpensive and worldwide available digital image analysis technique for histological fibrosis quantification in chronic hepatitis C. Journal of Viral Hepatitis. 21: 216–222.

    Article  CAS  PubMed  Google Scholar 

  7. Calvaruso V, Burroughs AK, Standish R, et al. (2009) Computer-Assisted Image Analysis of Liver Collagen: Relationship to Ishak Scoring and Hepatic Venous Pressure Gradient. Hepatology. 49(4): 1236-1244.

    Article  PubMed  Google Scholar 

  8. Poynard T, Lenaour G, Vaillant JC, et al. (2012) Liver Biopsy Analysis Has a Low Level of Performance for Diagnosis of Intermediate Stages of Fibrosis. Clin Gastroenterol Hepatol. 10: 657–663.

    Article  PubMed  Google Scholar 

  9. Martí-Bonmatí L, Delgado F. (2010) MR imaging in liver cirrhosis: classical and new approaches. Insights Imaging. 1(4): 233‐244.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Martí-Bonmatí L. (2002) MR contrast agents in hepatic cirrhosis and chronic hepatitis. Semin Ultrasound CT MR. 23(1): 101‐113.

    Article  PubMed  Google Scholar 

  11. Martí-Bonmatí L, Lonjedo E, Poyatos C, Casillas C. (1998) MnDPDP enhancement characteristics and differentiation between cirrhotic and noncirrhotic livers. Invest Radiol. 33(10): 717‐722.

    Article  PubMed  Google Scholar 

  12. Martí-Bonmatí L, Masiá L, Casillas C, et al. (1996) Differentiation of healthy from cirrhotic livers. Evaluation of parametric images after contrast administration in magnetic resonance imaging. Invest Radiol. 31(12): 768‐773.

    Article  PubMed  Google Scholar 

  13. Berzigotti A, França M, Martí-Aguado D, Martí-Bonmatí L. (2018) Imaging biomarkers in liver fibrosis. Radiologia. 60(1): 74-84.

    Article  CAS  PubMed  Google Scholar 

  14. Banerjee R, Pavlides M, Tunnicliffe EM, et al. (2014) Multiparametric magnetic resonance for the non-invasive diagnosis of liver disease. J Hepatol. 60(1): 69–77.

    Article  PubMed  PubMed Central  Google Scholar 

  15. França M, Marti-Bonmatí L, Alberich-Bayarri A, et al. (2017) Evaluation of fibrosis and inflammation in diffuse liver diseases using intravoxel incoherent motion diffusion-weighted MR imaging. Abdom Radiol. 42(2): 468-477.

    Article  Google Scholar 

  16. França M, Carvalho JG. (2020) MR imaging assessment and quantification of liver iron. Abdom Radiol. https://doi.org/10.1007/s00261-020-02574-8.

    Article  Google Scholar 

  17. França M, Alberich-Bayarri A, Martí-Bonmatí L, et al. (2017) Accurate simultaneous quantification of liver steatosis and iron overload in diffuse liver diseases with MRI. Abdom Radiol. 42:1434-1443.

    Article  Google Scholar 

  18. Yokoo T, Serai SD, Pirasteh A, et al. (2018) Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis. Radiology. 286(2): 486-498.

    Article  PubMed  Google Scholar 

  19. Angulo P, Kleiner DE, Dam-Larsen S, et al. (2015) Liver fibrosis, but no other histologic features, is associated with long-term outcomes of patients with nonalcoholic fatty liver disease. Gastroenterology 149: 389–397, e310.

    Google Scholar 

  20. Hagstrom H, Nasr P, Ekstedt M, et al. (2017) Fibrosis stage but not NASH predicts mortality and time to development of severe liver disease in biopsy-proven NAFLD. J Hepatol. 67: 1265–1273.

    Article  PubMed  Google Scholar 

  21. Vilar-Gomez E; Calzadilla-Bertot L, Wai-Sun Wong V, et al. (2018) Fibrosis Severity as a Determinant of Cause-Specific Mortality in Patients with Advanced Nonalcoholic Fatty Liver Disease: A Multi-National Cohort Study. Gastroenterology 155: 443–457.

    Article  PubMed  Google Scholar 

  22. Loomba R, Wolfson T, Ang B, et al. (2014) Magnetic Resonance Elastography Predicts Advanced Fibrosis in Patients with Nonalcoholic Fatty Liver Disease: A Prospective Study. Hepatology. 60(6): 1920-1928.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Le Bihan D, Ichikawa S, Motosugi U. (2017) Diffusion and Intravoxel Incoherent Motion MR Imaging-based Virtual Elastography: A Hypothesis-generating Study in the Liver. Radiology. 285(2): 609‐619.

    Article  PubMed  Google Scholar 

  24. Tosun M, Onal T, Uslu H, et al. (2020) Intravoxel incoherent motion imaging for diagnosing and staging the liver fibrosis and inflammation. Abdom Radiol. 45: 15–23.

    Article  Google Scholar 

  25. Gulbay M, Ciliz DS, Celikbas AK, et al. (2020) Intravoxel incoherent motion parameters in the evaluation of chronic hepatitis B virus-induced hepatic injury: fibrosis and capillarity changes. Abdom Radiol. https://doi.org/10.1007/s00261-020-02430-9.

    Article  Google Scholar 

  26. Zawada E, Serafin Z, Dybowska D, et al. (2019) Monoexponential and Biexponential Fitting of Diffusional Magnetic Resonance Imaging Signal Analysis for Prediction of Liver Fibrosis Severity. J Comput Assist Tomogr. 43(6): 857‐862.

    Article  PubMed  Google Scholar 

  27. Yoon JH, Lee JM, Lee KB, et al. (2019) Comparison of monoexponential, intravoxel incoherent motion diffusion-weighted imaging and diffusion kurtosis imaging for assessment of hepatic fibrosis. Acta Radiol. 60(12): 1593-1601.

    Article  PubMed  Google Scholar 

  28. Seo N, Chung YE, Park YN, et al. (2018) Liver fibrosis: stretched exponential model outperforms mono-exponential and bi-exponential models of diffusion-weighted MRI. Eur Radiol. 28(7): 2812-2822.

    Article  PubMed  Google Scholar 

  29. Yang L, Rao S, Wang W, et al. (2018) Staging liver fibrosis with DWI: is there an added value for diffusion kurtosis imaging? Eur Radiol. 28(7): 3041-3049.

    Article  PubMed  Google Scholar 

  30. Sandrasegaran K, Territo P, Elkady RM, et al. (2018) Does intravoxel incoherent motion reliably stage hepatic fibrosis, steatosis, and inflammation? Abdom Radiol. 43(3): 600-606.

    Article  Google Scholar 

  31. Leitão HS, Doblas S, Garteiser P, et al. (2017) Hepatic Fibrosis, Inflammation, and Steatosis: Influence on the MR Viscoelastic and Diffusion Parameters in Patients with Chronic Liver Disease. Radiology. 283(1): 98-107.

    Article  PubMed  Google Scholar 

  32. Pavlides M, Banerjee R, Tunnicliffe EM, et al (2017). Multiparametric magnetic resonance imaging for the assessment of non-alcoholic fatty liver disease severity. Liver Int. 37: 1065-1073.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Wu CH, Ho MC, Jeng YM, et al. (2015) Assessing hepatic fibrosis: comparing the intravoxel incoherent motion in MRI with acoustic radiation force impulse imaging in US. Eur Radiol. 25(12): 3552-3559.

    Article  PubMed  Google Scholar 

  34. Kocakoc E, Bakan AA, Poyrazoglu OK, et al. (2015) Assessment of Liver Fibrosis with Diffusion-Weighted Magnetic Resonance Imaging Using Different b-values in Chronic Viral Hepatitis. Med Princ Pract. 24(6): 522–526.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Singh A, Reddy D, Haris M, et al. (2015) T1ρ MRI of healthy and fibrotic human livers at 1.5 T. J Transl Med. 13: 292.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Parente DB, Paiva FF, Oliveira Neto JA, et al. (2015) Intravoxel Incoherent Motion Diffusion Weighted MR Imaging at 3.0 T: Assessment of Steatohepatitis and Fibrosis Compared with Liver Biopsy in Type 2 Diabetic Patients. PLoS ONE. 10(5): e0125653.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Murphy P, Hooker J, Ang B, et al. (2015) Associations between histologic features of nonalcoholic fatty liver disease (NAFLD) and quantitative diffusion-weighted MRI measurements in adults. J Magn Reson Imaging. 41(6): 1629–1638.

    Article  PubMed  Google Scholar 

  38. Allkemper T, Sagmeister F, Cicinnati V, et al. (2014) Evaluation of fibrotic liver disease with whole-liver T1ρ MR imaging: a feasibility study at 1.5 T. Radiology. 271(2): 408-415.

    Article  PubMed  Google Scholar 

  39. Takayama Y, Nishie A, Asayama Y, et al. (2015) T1 ρ Relaxation of the liver: A potential biomarker of liver function. J Magn Reson Imaging. 42(1): 188-95.

    Article  PubMed  Google Scholar 

  40. Yoon JH, Lee JM, Baek JH, et al. (2014) Evaluation of hepatic fibrosis using intravoxel incoherent motion in diffusion-weighted liver MRI. J Comput Assist Tomogr. 38(1): 110-116.

    Article  PubMed  Google Scholar 

  41. Chung SR, Lee SS, Kim N, et al. (2015) Intravoxel incoherent motion MRI for liver fibrosis assessment: a pilot study. Acta Radiol. 56(12): 1428-1436.

    Article  PubMed  Google Scholar 

  42. Bülow R, Mensel B, Meffert P, et al. (2013) Diffusion-weighted magnetic resonance imaging for staging liver fibrosis is less reliable in the presence of fat and iron. Eur Radiol. 23(5): 1281-1287.

    Article  PubMed  Google Scholar 

  43. Onur MR, Poyraz AK, Bozdag PG, et al. (2013) Diffusion weighted MRI in chronic viral hepatitis: correlation between ADC values and histopathological scores. Insights Imaging. 4(3): 339–345.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Vaziri-Bozorg SM, Ghasemi-Esfe AR, Khalilzadeh O, et al. (2012) Diffusion-weighted magnetic resonance imaging for diagnosis of liver fibrosis and inflammation in chronic viral hepatitis: the performance of low or high B values and small or large regions of interest. Can Assoc Radiol J. 63(4): 304-311.

    Article  PubMed  Google Scholar 

  45. Kovač JD, Daković M, Stanisavljević D, et al. (2012) Diffusion-weighted MRI versus transient elastography in quantification of liver fibrosis in patients with chronic cholestatic liver diseases. Eur J Radiol. 81(10): 2500-2506.

    Article  PubMed  Google Scholar 

  46. Pasquinelli F, Belli G, Mazzoni LN, et al. (2012) MR-diffusion imaging in assessing chronic liver diseases: does a clinical role exist? Radiol Med. 117(2): 242-253.

    Article  CAS  PubMed  Google Scholar 

  47. Watanabe H, Kanematsu M, Goshima S, et al. (2011) Staging hepatic fibrosis: comparison of gadoxetate disodium-enhanced and diffusion-weighted MR imaging--preliminary observations. Radiology. 259(1): 142-150.

    Article  PubMed  Google Scholar 

  48. Wang Y, Ganger DR, Levitsky J, et al. (2011) Assessment of chronic hepatitis and fibrosis: comparison of MR elastography and diffusion-weighted imaging. AJR Am J Roentgenol. 196(3): 553–561.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Fujimoto K, Tonan T, Azuma S, et al. (2011) Evaluation of the mean and entropy of apparent diffusion coefficient values in chronic hepatitis C: correlation with pathologic fibrosis stage and inflammatory activity grade. Radiology. 258(3): 739-48.

    Article  PubMed  Google Scholar 

  50. Sandrasegaran K, Akisik FM, Lin C, et al. (2009) Value of diffusion-weighted MRI for assessing liver fibrosis and cirrhosis. AJR Am J Roentgenol. 193(6):1556-60.

    Article  PubMed  Google Scholar 

  51. Taouli B, Chouli M, Martin AJ, et al. (2008) Chronic hepatitis: role of diffusion-weighted imaging and diffusion tensor imaging for the diagnosis of liver fibrosis and inflammation. J Magn Reson Imaging. 28(1): 89-95.

    Article  PubMed  Google Scholar 

  52. Taouli B, Tolia AJ, Losada M, et al. (2007) Diffusion-weighted MRI for quantification of liver fibrosis: preliminary experience. AJR Am J Roentgenol. 189(4): 799-806.

    Article  PubMed  Google Scholar 

  53. Girometti R, Furlan A, Bazzocchi M, et al. (2007) Diffusion-weighted MRI in evaluating liver fibrosis: a feasibility study in cirrhotic patients. Radiol Med. 112(3): 394-408.

    Article  CAS  PubMed  Google Scholar 

  54. Lewin M, Poujol-Robert A, Boëlle PY, et al. (2007) Diffusion-weighted magnetic resonance imaging for the assessment of fibrosis in chronic hepatitis C. Hepatology. 46(3): 658-665.

    Article  CAS  PubMed  Google Scholar 

  55. Koinuma M, Ohashi I, Hanafusa K, Shibuya H. (2005) Apparent diffusion coefficient measurements with diffusion-weighted magnetic resonance imaging for evaluation of hepatic fibrosis. J Magn Reson Imaging. 22(1): 80-5.

    Article  PubMed  Google Scholar 

  56. Boulanger Y, Amara M, Lepanto L, et al. (2003) Diffusion-weighted MR imaging of the liver of hepatitis C patients. NMR Biomed. 16(3): 132-6.

    Article  PubMed  Google Scholar 

  57. Martí-Bonmatí L, Talens A, del Olmo J, et al. (1993) Chronic hepatitis and cirrhosis: evaluation by means of MR imaging with histologic correlation. Radiology. 188(1): 37-43.

    Article  PubMed  Google Scholar 

  58. Sheng RF, Wang HQ, Yang L, et al. (2017) Diffusion kurtosis imaging and diffusion-weighted imaging in assessment of liver fibrosis stage and necroinflammatory activity. Abdom Radiol. 42(4): 1176-1182.

    Article  Google Scholar 

  59. Fram EK, Herfkens R J, Johnson GA, et al. (1987) Rapid calculation of T1 using variable flip angle gradient refocused imaging. Magnetic resonance imaging. 5(3): 201-208.

    Article  CAS  PubMed  Google Scholar 

  60. Klein S, Staring M, Murphy K, et al. (2010) Elastix: A Toolbox for Intensity-Based Medical Image Registration. IEEE Trans Med Imaging. 29: 196–205.

    Article  PubMed  Google Scholar 

  61. Castera L, Friedrich-Rust M, Loomba R. (2019) Noninvasive Assessment of Liver Disease in Patients With Nonalcoholic Fatty Liver Disease. Gastroenterology 156: 1264–1281.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Boursier J, Vergniol J, Guillet A, et al. (2016) Diagnostic accuracy and prognostic significance of blood fibrosis tests and liver stiffness measurement by FibroScan in non-alcoholic fatty liver disease. J Hepatol. 65(3): 570-578.

    Article  PubMed  Google Scholar 

  63. Papalavrentios L, Sinakos E, Chourmouzi D, et al. (2013) 3 Tesla diffusion‐weighted MRI for assessing liver fibrosis in nonalcoholic fatty liver disease. Hepatology, 58: 449-450.

    Article  PubMed  Google Scholar 

  64. Rauscher I, Eiber M, Ganter C, et al. (2014) Evaluation of T1ρ as a potential MR biomarker for liver cirrhosis: Comparison of healthy control subjects and patients with liver cirrhosis. Eur J Radiol. 83: 900-904.

    Article  PubMed  Google Scholar 

  65. Wang YX, Yuan J. (2014) Evaluation of liver fibrosis with T1ρ MR imaging. Quant Imaging Med Surg. 4(3): 152-5

    PubMed  PubMed Central  Google Scholar 

  66. Xie S, Li Q, Cheng Y, et al. (2017) Impact of liver fibrosis and fatty liver on T1rho measurements: a prospective study. Korean J Radiol. 18: 898-905.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Deng M, Zhao F, Yuan J, et al. (2012) Liver T1ρ MRI measurement in healthy human subjects at 3 T: a preliminary study with a two-dimensional fast-field echo sequence. Br J Radiol. 85: e590-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Guyatt GH, Oxman AD, Vist GE, et al. (2008) GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 336: 924–926.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Kennedy P, Wagner M, Castera L, et al. (2018) Quantitative elastography methods in liver disease: current evidence and future directions. Radiology. 286: 738–63.

    Article  PubMed  PubMed Central  Google Scholar 

  70. Loomba R, Cui J, Wolfson T, et al. (2016) Novel 3D magnetic resonance elastography for the noninvasive diagnosis of advanced fibrosis in NAFLD: a prospective study. Am J Gastroenterol. 111: 986–894.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Vilar-Gomez E, Chalasani (2018) N. Non-invasive assessment of non-alcoholic fatty liver disease: Clinical prediction rules and blood-based biomarkers. J Hepatol. 68(2): 305-315.

    Article  CAS  PubMed  Google Scholar 

  72. Batts KP, Ludwig J. (1995) Chronic hepatitis: an update on terminology and reporting. Am J Surg Pathol. 19: 1409–1417.

    Article  CAS  PubMed  Google Scholar 

  73. Kleiner DE, Brunt EM, Van Natta M, et al. (2005) Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology. 41: 1313-1321.

    Article  PubMed Central  Google Scholar 

  74. Brunt EM, Kleiner DE, Wilson LA, et al. (2018) Improvements in histologic features and diagnosis associated with improvement in fibrosis in nonalcoholic steatohepatitis: results from the nonalcoholic steatohepatitis clinical research network treatment trials. Hepatology. 70: 522‐531.

    Google Scholar 

  75. Brunt EM, Kleiner DE, Wilson LA, et al. (2009) Portal chronic inflammation in nonalcoholic fatty liver disease (NAFLD): a histologic marker of advanced NAFLD‐clinicopathologic correlations from the nonalcoholic steatohepatitis clinical research network. Hepatology. 49: 809‐820.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Singh S, Allen AM, Wang Z, et al. (2015) Fibrosis progression in nonalcoholic fatty liver vs nonalcoholic steatohepatitis: a systematic review and meta-analysis of paired biopsy studies. Clin Gastroenterol Hepatol. 13(04): 643–654.

    Article  PubMed  Google Scholar 

  77. Sanyal AJ, Banas C, Sargeant C, et al. (2006) Similarities and differences in outcomes of cirrhosis due to nonalcoholic steatohepatitis and hepatitis C. Hepatology. 43(04): 682–689.

    Article  PubMed  Google Scholar 

  78. Nelson JE, Wilson L, Brunt EM, et al. (2011) Relationship between the pattern of hepatic iron deposition and histological severity in nonalcoholic fatty liver disease. Hepatology. 53(2): 448–457.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

DMA is the holder of a Río Hortega award (CM19/00212), Instituto de Salud Carlos III, Ministerio de Ciencia e Innovación, Spain.

Funding

This study was funded by the Spanish Ministry of Science and innovation, Instituto de Salud Carlos III (PI19/0380), and GILEAD Sciences (Grant Number: GLD19/00050). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Gastroenterology and Hepatology, Clinic University Hospital, INCLIVA Health Research Institute, Valencia, Spain

    D. Marti-Aguado

  2. Biomedical Imaging Research Group (GIBI230 and PREBI), and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), La Fe Health Research Institute, Valencia, Spain

    D. Marti-Aguado, A. Rodríguez-Ortega, A. Alberich-Bayarri & L. Marti-Bonmati

  3. Quantitative Imaging Biomarkers in Medicine, QUIBIM SL, Valencia, Spain

    A. Alberich-Bayarri

  4. Radiology Department, La Fe University and Polytechnic Hospital, Av Fernando Abril Martorell 106, 46026, Valencia, Spain

    L. Marti-Bonmati

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  1. D. Marti-Aguado
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  3. A. Alberich-Bayarri
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Correspondence to L. Marti-Bonmati.

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Marti-Aguado, D., Rodríguez-Ortega, A., Alberich-Bayarri, A. et al. Magnetic Resonance imaging analysis of liver fibrosis and inflammation: overwhelming gray zones restrict clinical use. Abdom Radiol 45, 3557–3568 (2020). https://doi.org/10.1007/s00261-020-02713-1

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