Skip to main content
SpringerLink
Log in

Does intravoxel incoherent motion reliably stage hepatic fibrosis, steatosis, and inflammation?

  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Objective

To investigate the usefulness of intravoxel incoherent motion (IVIM) in determining the severity of hepatic fibrosis, steatosis, and inflammation in patients with chronic liver disease.

Methods

Forty-nine patients who had liver MRI with IVIM sequence and liver biopsy within three months of MRI were enrolled. A reviewer, blinded to histology, placed regions of interest of 1–2 cm2 in the right liver lobe. In addition, the first twenty patients were assessed with a second reviewer. Perfusion fraction (f), pseudodiffusion coefficient (D fast), true diffusion coefficient (D slow), and apparent diffusion coefficient (ADC) were calculated from normalized signal intensities that were fitted into a biexponential model. Errors in the model were minimized with global stochastic optimization using Simulated Annealing. ANOVA with post hoc Tukey–Kramer test and multivariate generalized linear model analysis were performed, using histological findings as the gold standard.

Results

The most common etiologies for liver disease were hepatitis C and alcohol, accounting together for 76% (37/49) of patients. Low-grade fibrosis (F0, F1), hepatic steatosis, and inflammation were seen in 24% (12/49), 31% (15/49), and 29% (14/49) of patients, respectively. The interobserver correlation was poor for D fast and D slow (0.105, 0.173) and moderate for f and ADC (0.461, 0.418). ANOVA showed a strong inverse association between D fast and liver fibrosis grade (p = 0.001). A weak inverse association was seen between ADC and hepatic steatosis (p = 0.059). Multivariate general linear model revealed that the only significant association between IVIM parameters and pathological features was between D fast and fibrosis. On ROC curve analysis, D fast < 23.4 × 10−3 mm2/s had a sensitivity of 82.8% and a specificity of 64.3% in predicting high-grade fibrosis.

Conclusion

D fast has the strongest association with hepatic fibrosis but has weak interobserver correlation. IVIM parameters were not significantly associated with hepatic inflammation or steatosis.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Falize Ludivine, Guillygomarc’h Anne, Perrin Michele, et al. (2006) Reversibility of hepatic fibrosis in treated genetic hemochromatosis: a study of 36 cases†. Hepatology 44(2):472–477

    Article  PubMed  Google Scholar 

  2. Marcellin P, Gane E, Buti M, et al. (2013) Regression of cirrhosis during treatment with tenofovir disoproxil fumarate for chronic hepatitis B: a 5-year open-label follow-up study. Lancet 9(381):468–475

    Article  Google Scholar 

  3. Ichikawa S, Motosugi U, Morisaka H, et al. (2015) MRI-based staging of hepatic fibrosis: comparison of intravoxel incoherent motion diffusion-weighted imaging with magnetic resonance elastography. J Magn Reson Imaging 42(1):204–210

    Article  PubMed  Google Scholar 

  4. 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 

  5. Bohte AE, de Niet A, Jansen L, et al. (2014) Non-invasive evaluation of liver fibrosis: a comparison of ultrasound-based transient elastography and MR elastography in patients with viral hepatitis B and C. Eur Radiol 24(3):638–648

    Article  PubMed  Google Scholar 

  6. Haimerl M, Verloh N, Zeman F, et al. (2013) Assessment of clinical signs of liver cirrhosis using T1 mapping on Gd-EOB-DTPA-enhanced 3T MRI. PLoS ONE 8(12):e85658

    Article  PubMed  PubMed Central  Google Scholar 

  7. Bakan AA, Inci E, Bakan S, Gokturk S, Cimilli T (2012) Utility of diffusion-weighted imaging in the evaluation of liver fibrosis. Eur Radiol 22(3):682–687

    Article  PubMed  Google Scholar 

  8. 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 

  9. Jajamovich GH, Calcagno C, Dyvorne HA, Rusinek H, Taouli B (2014) DCE-MRI of the liver: reconstruction of the arterial input function using a low dose pre-bolus contrast injection. PLoS ONE 9(12):e115667

    Article  PubMed  PubMed Central  Google Scholar 

  10. Xu PJ, Yan FH, Wang JH, Lin J, Ji Y (2009) Added value of breathhold diffusion-weighted MRI in detection of small hepatocellular carcinoma lesions compared with dynamic contrast-enhanced MRI alone using receiver operating characteristic curve analysis. J Magn Reson Imaging 29(2):341–349

    Article  PubMed  Google Scholar 

  11. Bulow 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 

  12. 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):1556–1560

    Article  PubMed  Google Scholar 

  13. 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(2):203–207

    Article  PubMed  Google Scholar 

  14. Wang QB, Zhu H, Liu HL, Zhang B (2012) Performance of magnetic resonance elastography and diffusion-weighted imaging for the staging of hepatic fibrosis: a meta-analysis. Hepatology 56(1):239–247

    Article  PubMed  Google Scholar 

  15. Le Bihan DBE, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M (1986) MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161:401–407

    Article  PubMed  Google Scholar 

  16. Koh DM, Collins DJ, Orton MR (2011) Intravoxel incoherent motion in body diffusion-weighted MRI: reality and challenges. AJR Am J Roentgenol 196(6):1351–1361

    Article  PubMed  Google Scholar 

  17. 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 

  18. Franca M, Marti-Bonmati 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 (NY) 42(2):468–477

    Article  Google Scholar 

  19. Lu PX, 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(12):e113846

    Article  PubMed  PubMed Central  Google Scholar 

  20. Luciani A, Vignaud A, Cavet M, et al. (2008) Liver cirrhosis: intravoxel incoherent motion MR imaging–pilot study. Radiology 249(3):891–899

    Article  PubMed  Google Scholar 

  21. 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(3):589–600

    Article  PubMed  PubMed Central  Google Scholar 

  22. 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 

  23. 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 

  24. Goffe WL, Ferrier GD, Rogers J (1994) Global optimization of statistical functions with simulated annealing. J Econom 60(1–2):65–99

    Article  Google Scholar 

  25. Bedossa P, Poynard T (1996) An algorithm for the grading of activity in chronic hepatitis C. The METAVIR Cooperative Study Group. Hepatology 24(2):289–293

    Article  CAS  PubMed  Google Scholar 

  26. Goodman ZD (2007) Grading and staging systems for inflammation and fibrosis in chronic liver diseases. J Hepatol 47(4):598–607

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  28. Lewin MP-RA, Boelle PY, et al. (2007) Diffusion-weighted magnetic resonance imaging for the assessment of fibrosis in chronic hepatitis C. Hepatology 46:658–665

    Article  CAS  PubMed  Google Scholar 

  29. Girometti RFA, Esposito G, et al. (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 28:411–419

    Article  PubMed  Google Scholar 

  30. Bonekamp S, Torbenson MS, Kamel IR (2011) Diffusion-weighted magnetic resonance imaging for the staging of liver fibrosis. J Clin Gastroenterol 45(10):885–892

    Article  PubMed  PubMed Central  Google Scholar 

  31. Cece H, Ercan A, Yildiz S, et al. (2013) The use of DWI to assess spleen and liver quantitative ADC changes in the detection of liver fibrosis stages in chronic viral hepatitis. Eur J Radiol 82(8):e307–e312

    Article  PubMed  Google Scholar 

  32. Tosun M, Inan N, Sarisoy HT, et al. (2012) Diagnostic performance of conventional diffusion weighted imaging and diffusion tensor imaging for the liver fibrosis and inflammation. Eur J Radiol 82(2):203–207

    Article  PubMed  Google Scholar 

  33. Koinuma MOI, 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):80–85

    Article  PubMed  Google Scholar 

  34. Chow AM, Gao DS, Fan SJ, et al. (2012) Liver fibrosis: an intravoxel incoherent motion (IVIM) study. J Magn Reson Imaging 36(1):159–167

    Article  PubMed  Google Scholar 

  35. 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

    Article  PubMed  Google Scholar 

  36. Zhang SX, Jia QJ, Zhang ZP, et al. (2014) Intravoxel incoherent motion MRI: emerging applications for nasopharyngeal carcinoma at the primary site. Eur Radiol 24(8):1998–2004

    Article  PubMed  PubMed Central  Google Scholar 

  37. Andreou A, Koh DM, Collins DJ, et al. (2013) 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(2):428–434

    Article  CAS  PubMed  Google Scholar 

  38. Kakite S, Dyvorne H, Besa C, et al. (2015) Hepatocellular carcinoma: short-term reproducibility of apparent diffusion coefficient and intravoxel incoherent motion parameters at 3.0T. J Magn Reson Imaging 41(1):149–156

    Article  PubMed  Google Scholar 

  39. Lee Y, Lee SS, Kim N, et al. (2015) Intravoxel incoherent motion diffusion-weighted MR imaging of the liver: effect of triggering methods on regional variability and measurement repeatability of quantitative parameters. Radiology 274(2):405–415

    Article  PubMed  Google Scholar 

  40. Li YT, Cercueil JP, Yuan J, et al. (2017) Liver intravoxel incoherent motion (IVIM) magnetic resonance imaging: a comprehensive review of published data on normal values and applications for fibrosis and tumor evaluation. Quant Imaging Med Surg 7(1):59–78

    Article  PubMed  PubMed Central  Google Scholar 

  41. Hansmann J, Hernando D, Reeder SB (2013) Fat confounds the observed apparent diffusion coefficient in patients with hepatic steatosis. Magn Reson Med 69(2):545–552

    Article  PubMed  Google Scholar 

  42. Poyraz AK, Onur MR, Kocakoc E, Ogur E (2012) Diffusion-weighted MRI of fatty liver. J Magn Reson Imaging 35(5):1108–1111

    Article  PubMed  Google Scholar 

  43. 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(5):1209–1217

    Article  PubMed  Google Scholar 

Download references

Author information

Author notes

  1. Reem M. Elkady

    Present address: Department of Radiology, Assiut University, Assiut, Egypt

  2. Yuning Lin

    Present address: Department of Radiology, Fuzhou General Hospital, Fuzhou, China

Authors and Affiliations

  1. Department of Radiology, Indiana University School of Medicine, 550 N University Blvd, UH 0279, Indianapolis, IN, 46202, USA

    Kumaresan Sandrasegaran, Paul Territo, Reem M. Elkady, Yuning Lin, Pauley Gasparis, Gitasree Borthakur & Chen Lin

Authors
  1. Kumaresan Sandrasegaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paul Territo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Reem M. Elkady
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuning Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pauley Gasparis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gitasree Borthakur
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chen Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kumaresan Sandrasegaran.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sandrasegaran, K., Territo, P., Elkady, R.M. et al. Does intravoxel incoherent motion reliably stage hepatic fibrosis, steatosis, and inflammation?. Abdom Radiol 43, 600–606 (2018). https://doi.org/10.1007/s00261-017-1263-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-017-1263-8

Keywords

Search

Navigation