Skip to main content
Log in

Evaluation of fibrosis and inflammation in diffuse liver diseases using intravoxel incoherent motion diffusion-weighted MR imaging

  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

The purpose of the study was to evaluate the role of intravoxel incoherent motion (IVIM) diffusion model for the assessment of liver fibrosis and inflammation in diffuse liver disorders, also considering the presence of liver steatosis and iron deposits.

Methods

Seventy-four patients were included, with liver biopsy and a 3 Tesla abdominal magnetic resonance imaging examination, with an IVIM diffusion-weighted sequence (single-shot spin-echo echo-planar sequence, with gradient reversal fat suppression; 6 b-values: 0, 50, 200, 400, 600, and 800 s/mm2). Histological evaluation comprised the Ishak modified scale, for grading inflammation and fibrosis, plus steatosis and iron loading classification. The liver apparent diffusion coefficient (ADC) and IVIM parameters (D, D*, f) were calculated from the IVIM images. The relationship between IVIM parameters and histopathological scores were evaluated by ANOVA and Spearman correlation tests. A test–retest experiment assessed reproducibility and repeatability in 10 healthy volunteers and 10 randomly selected patient studies.

Results

ADC and f values were lower with higher fibrosis stages (p = 0.009, p = 0.006, respectively) and also with higher necro-inflammatory activity grades (p = 0.02, p = 0.017, respectively). Considered together, only fibrosis presented a significant effect on ADC and f measurements (p < 0.05), whereas inflammation had no significant effect (p > 0.05). A mild correlation was found between ADC and f with fibrosis (R S = −0.32 and R S = −0.38; p < 0.05) and inflammation (R S = −0.31 and R S = −0.32, p < 0.05; respectively). The AUROC for ADC and f measurements with the different dichotomizations between fibrosis or inflammation grades were only fair (0.670 to 0.749, p < 0.05). Neither D nor D* values were significantly different between liver fibrosis or inflammation grades. D measurements were significantly different across histologic grades of steatosis (p < 0.001) and iron overload (p < 0.001), whereas f measurements showed significant differences across histologic steatosis grades (p = 0.005). There was an excellent agreement between the different readers for ADC, f, and D.

Conclusions

Although fibrosis presented a significant effect on ADC and f, IVIM measurements are not accurate enough to stage liver fibrosis or necro-inflammatory activity in diffuse liver diseases. D values were influenced by steatosis and iron overload.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Ellis EL, Mann DA (2012) Clinical evidence for the regression of liver fibrosis. J Hepatol 56:1171–1180. doi:10.1016/j.jhep.2011.09.024

    Article  PubMed  Google Scholar 

  2. Myers RP, Fong A, Shaheen AAM (2008) Utilization rates, complications and costs of percutaneous liver biopsy: a population-based study including 4275 biopsies. Liver Int 28:705–712. doi:10.1111/j.1478-3231.2008.01691.x

    Article  PubMed  Google Scholar 

  3. Bedossa P, Dargère D, Paradis V (2003) Sampling variability of liver fibrosis in chronic hepatitis C. Hepatology 38:1449–1457. doi:10.1016/j.hep.2003.09.022

    Article  PubMed  Google Scholar 

  4. Lewin MM, Poujol-Robert AA, Boëlle P-YP, et al. (2007) Diffusion-weighted magnetic resonance imaging for the assessment of fibrosis in chronic hepatitis C. Hepatology 46:658–665. doi:10.1002/hep.21747

    Article  CAS  PubMed  Google Scholar 

  5. Luciani A, Vignaud A, Cavet M, et al. (2008) Liver cirrhosis: Intravoxel incoherent motion MR Imaging—Pilot Study. Radiology 249:891–899. doi:10.1148/radiol.2493080080

    Article  PubMed  Google Scholar 

  6. Taouli B, Tolia AJ, Losada M, et al. (2007) Diffusion-weighted MRI for quantification of liver fibrosis: preliminary experience. AJR Am J Roentgenol 189:799–806. doi:10.2214/AJR.07.2086

    Article  PubMed  Google Scholar 

  7. 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 28:89–95. doi:10.1002/jmri.21227

    Article  PubMed  Google Scholar 

  8. Sandrasegaran K, Akisik FM, Lin C, et al. (2009) Value of diffusion-weighted MRI for assessing liver fibrosis and cirrhosis. AJR Am J Roentgenol 193:1556–1560. doi:10.2214/AJR.09.2436

    Article  PubMed  Google Scholar 

  9. Bakan AA, Inci E, Bakan S, et al. (2011) Utility of diffusion-weighted imaging in the evaluation of liver fibrosis. Eur Radiol 22:682–687. doi:10.1007/s00330-011-2295-z

    Article  PubMed  Google Scholar 

  10. Taouli B, Koh DM (2009) Diffusion-weighted MR Imaging of the Liver. Radiology 254:47–66. doi:10.1148/radiol.09090021

    Article  Google Scholar 

  11. Yamada I, Aung W, Himeno Y, et al. (1999) Diffusion coefficients in abdominal organs and hepatic lesions: evaluation with intravoxel incoherent motion echo-planar MR imaging. Radiology 210:617–623

    Article  CAS  PubMed  Google Scholar 

  12. Le Bihan D, Breton E, Lallemand D, et al. (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168:497–505. doi:10.1148/radiology.168.2.3393671

    Article  PubMed  Google Scholar 

  13. 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 38:110–116. doi:10.1097/RCT.0b013e3182a589be

    Article  PubMed  Google Scholar 

  14. Lu P-X, Huang H, Yuan J, et al. (2014) Decreases in molecular diffusion, perfusion fraction and perfusion-related diffusion in fibrotic livers: A prospective clinical intravoxel incoherent motion MR imaging study. PLoS One 9:e113846. doi:10.1371/journal.pone.0113846.t002

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hu G, Chan Q, Quan X, et al. (2014) Intravoxel incoherent motion MRI evaluation for the staging of liver fibrosis in a rat model. J Magn Reson Imaging 42:331–339. doi:10.1002/jmri.24796

    Article  PubMed  Google Scholar 

  16. Chow AM, Gao DS, Fan SJ, et al. (2012) Liver fibrosis: an intravoxel incoherent motion (IVIM) study. J Magn Reson Imaging 36:159–167. doi:10.1002/jmri.23607

    Article  PubMed  Google Scholar 

  17. Chung SR, Lee SS, Kim N, et al. (2014) Intravoxel incoherent motion MRI for liver fibrosis assessment: a pilot study. Acta Radiol 56:1428–1436. doi:10.1177/0284185114559763

    Article  PubMed  Google Scholar 

  18. Zhang Y, Jin N, Deng J, et al. (2013) Intra-voxel incoherent motion MRI in rodent model of diethylnitrosamine-induced liver fibrosis. Magn Reson Imaging 31:1017–1021. doi:10.1016/j.mri.2013.03.007

    Article  PubMed  PubMed Central  Google Scholar 

  19. Saxena R (2011) Microscopic anatomy, basic terms, and elemental lesions. In: Saxena R (ed) Practical hepatic pathology: a diagnostic approach, 1st edn. Philadelphia: Elsevier Saunders, pp 3–28

    Chapter  Google Scholar 

  20. Wang Q-B, Zhu H, Liu H-L, Zhang B (2012) Performance of magnetic resonance elastography and diffusion-weighted imaging for the staging of hepatic fibrosis: a meta-analysis. Hepatology 56:239–247. doi:10.1002/hep.25610

    Article  PubMed  Google Scholar 

  21. Tosun M, Inan N, Sarisoy HT, et al. (2013) Diagnostic performance of conventional diffusion weighted imaging and diffusion tensor imaging for the liver fibrosis and inflammation. Eur J Radiol 82:203–207. doi:10.1016/j.ejrad.2012.09.009

    Article  PubMed  Google Scholar 

  22. 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 258:739–748. doi:10.1148/radiol.10100853

    Article  PubMed  Google Scholar 

  23. Bülow R, Mensel B, Meffert P, et al. (2012) Diffusion-weighted magnetic resonance imaging for staging liver fibrosis is less reliable in the presence of fat and iron. Eur Radiol 23:1281–1287. doi:10.1007/s00330-012-2700-2

    Article  PubMed  Google Scholar 

  24. Guiu B, Petit JM, Capitan V, et al. (2012) Intravoxel incoherent motion diffusion-weighted imaging in nonalcoholic fatty liver disease: a 3.0-T MR Study. Radiology 265:96–103. doi:10.1148/radiol.12112478

    Article  PubMed  Google Scholar 

  25. Lee JT, Liau J, Murphy P, et al. (2012) Cross-sectional investigation of correlation between hepatic steatosis and IVIM perfusion on MR imaging. Magn Reson Imaging 30:572–578. doi:10.1016/j.mri.2011.12.013

    Article  PubMed  PubMed Central  Google Scholar 

  26. Hansmann J, Hernando D, Reeder SB (2012) Fat confounds the observed apparent diffusion coefficient in patients with hepatic steatosis. Magn Reson Med 69:545–552. doi:10.1002/mrm.24535

    Article  PubMed  PubMed Central  Google Scholar 

  27. Leitão HS, Doblas S, d’Assignies G, et al. (2012) Fat deposition decreases diffusion parameters at MRI: a study in phantoms and patients with liver steatosis. Eur Radiol 23:461–467. doi:10.1007/s00330-012-2626-8

    Article  PubMed  Google Scholar 

  28. 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 

  29. 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. doi:10.1002/hep.20701

    Article  PubMed  Google Scholar 

  30. Deugnier Y, Turlin B (2007) Pathology of hepatic iron overload. WJG 13:4755–4760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Papalavrentios L, Sinakos E, Chourmouzi D, et al. (2015) Value of 3 Tesla diffusion-weighted magnetic resonance imaging for assessing liver fibrosis. Ann Gastroenterol 28:118–123

    PubMed  PubMed Central  Google Scholar 

  32. Bonekamp S, Torbenson MS, Kamel IR (2011) Diffusion-weighted magnetic resonance imaging for the staging of liver fibrosis. J Clin Gastroenterol 45:885–892. doi:10.1097/MCG.0b013e318223bd2c

    Article  PubMed  PubMed Central  Google Scholar 

  33. Patel J, Sigmund EE, Rusinek H, et al. (2010) Diagnosis of cirrhosis with intravoxel incoherent motion diffusion MRI and dynamic contrast-enhanced MRI alone and in combination: Preliminary experience. J Magn Reson Imaging 31:589–600. doi:10.1002/jmri.22081

    Article  PubMed  PubMed Central  Google Scholar 

  34. Murphy P, Hooker J, Ang B, et al. (2014) Associations between histologic features of nonalcoholic fatty liver disease (NAFLD) and quantitative diffusion-weighted MRI measurements in adults. J Magn Reson Imaging 41:1629–1638. doi:10.1002/jmri.24755

    Article  PubMed  PubMed Central  Google Scholar 

  35. 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:e0125653. doi:10.1371/journal.pone.0125653.t004

    Article  PubMed  PubMed Central  Google Scholar 

  36. Chandarana H, Do RKG, Mussi TC, et al. (2012) The effect of liver iron deposition on hepatic apparent diffusion coefficient values in cirrhosis. AJR Am J Roentgenol 199:803–808. doi:10.2214/AJR.11.7541

    Article  PubMed  Google Scholar 

  37. Leporq B, Saint-Jalmes H, Rabrait C, et al. (2015) Optimization of intra-voxel incoherent motion imaging at 3.0 Tesla for fast liver examination. J Magn Reson Imaging 41:1209–1217. doi:10.1002/jmri.24693

    Article  PubMed  Google Scholar 

  38. Andreou A, Koh DM, Collins DJ, et al. (2012) Measurement reproducibility of perfusion fraction and pseudodiffusion coefficient derived by intravoxel incoherent motion diffusion-weighted MR imaging in normal liver and metastases. Eur Radiol 23:428–434. doi:10.1007/s00330-012-2604-1

    Article  PubMed  Google Scholar 

  39. Desmet VJ (2003) Knodell RG, Ishak KG, Black WC, Chen TS, Craig R, Kaplowitz N, Kiernan TW, Wollman J. Formulation and application of a numerical scoring system for assessing histological activity in asymptomatic chronic active hepatitis [Hepatology 1981;1:431–435]. J Hepatol 38:382–386. doi:10.1016/S0168-8278(03)00005-9

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

This work was partially funded by a research Grant from the Teaching and Research Department of Centro Hospitalar do Porto (DEFI:309/12(213-DEFI/251-CES)) and from a Spanish Ministry of Health and Carlos III Health Institute funding grant (PI12/01262). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Manuela França.

Ethics declarations

Conflicts of interest

Javier Sanchez Gonzalez is employee at Philips Healthcare Iberia. Angel Alberich Bayarri and Luis Martí-Bonmatí are co-founders of QUIBIM SME. The remaining authors declare that they have no conflicts of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Written informed consent was obtained from all patients.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

França, M., Martí-Bonmatí, L., Alberich-Bayarri, Á. et al. Evaluation of fibrosis and inflammation in diffuse liver diseases using intravoxel incoherent motion diffusion-weighted MR imaging. Abdom Radiol 42, 468–477 (2017). https://doi.org/10.1007/s00261-016-0899-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-016-0899-0

Keywords

Navigation