Skip to main content

Advertisement

Log in

Accurate IVIM model-based liver lesion characterisation can be achieved with only three b-value DWI

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objective

The objective of this study was to evaluate a simplified intravoxel incoherent motion (IVIM) approach of diffusion-weighted imaging (DWI) with four b-values for liver lesion characterisation at 1.5 T.

Methods

DWI data from a respiratory-gated MRI sequence with b = 0, 50, 250, 800 s/mm2 were retrospectively analysed in 173 lesions and 40 healthy livers. The apparent diffusion coefficient ADC = ADC(0,800) and IVIM-based parameters D1′ = ADC(50,800), D2′ =ADC(250,800), f1′, f2′, D*′, ADClow = ADC(0,50), and ADCdiff=ADClow-D2′ were calculated voxel-wise without fitting procedures. Differences between lesion groups were investigated.

Results

Focal nodular hyperplasias were best discriminated from all other lesions by f1′ with an area under the curve (AUC) of 0.989. Haemangiomas were best discriminated by D1′ (AUC of 0.994). For discrimination between malignant and benign lesions, ADC(0,800) and D1′ were best suited (AUC of 0.915 and 0.858, respectively). Discriminatory power was further increased by using a combination of D1′ and f1′.

Conclusion

IVIM parameters D and f approximated from three b-values provided more discriminatory power between liver lesions than ADC determined from two b-values. The use of b = 0, 50, 800 s/mm2 was superior to that of b = 0, 250, 800 s/mm2. The acquisition of four instead of three b-values has no further benefit for lesion characterisation.

Key Points

Diffusion and perfusion characteristics are assessable with only three b-values.

Association of b = 0, 50, 800 s/mm2is superior to b = 0, 250, 800 s/mm2.

A fourth acquired b-value has no benefit for differential diagnosis.

For liver lesion characterisation, simplified IVIM analysis is superior to ADC determination.

Simplified IVIM approach guarantees numerically stable, voxel-wise results and short acquisition times.

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

Similar content being viewed by others

Abbreviations

ADC:

Apparent diffusion coefficient

AUC:

Area under the curve

CCC:

Cholangiocellular carcinoma

DWI:

Diffusion-weighted imaging

FNH:

Focal nodular hyperplasia

HCC:

Hepatocellular carcinoma

IVIM:

Intravoxel incoherent motion

REF:

Reference tissue

ROI:

Region of interest

References

  1. Taouli B (2012) Diffusion-weighted MR imaging for liver lesion characterization: a critical look. Radiology 262:378–380

    Article  Google Scholar 

  2. Taouli B, Koh DM (2010) Diffusion-weighted MR imaging of the liver. Radiology 254:47–66

    Article  Google Scholar 

  3. Takahara T, Kwee TC (2012) Low b-value diffusion-weighted imaging: emerging applications in the body. J Magn Reson Imaging 35:1266–1273

    Article  Google Scholar 

  4. Kwee TC, Takahara T (2011) Diffusion-weighted MRI for detecting liver metastases: importance of the b-value. Eur Radiol 21:150

    Article  Google Scholar 

  5. Coenegrachts K, Delanote J, Ter Beek L et al (2009) Evaluation of true diffusion, perfusion factor, and apparent diffusion coefficient in non-necrotic liver metastases and uncomplicated liver hemangiomas using black-blood echo planar imaging. Eur J Radiol 69:131–138

    Article  Google Scholar 

  6. Parikh T, Drew SJ, Lee VS et al (2008) Focal liver lesion detection and characterization with diffusion-weighted MR imaging: comparison with standard breath-hold T2-weighted imaging. Radiology 246:812–822

    Article  Google Scholar 

  7. Gourtsoyianni S, Papanikolaou N, Yarmenitis S et al (2008) Respiratory gated diffusion-weighted imaging of the liver: value of apparent diffusion coefficient measurements in the differentiation between most commonly encountered benign and malignant focal liver lesions. Eur Radiol 18:486–492

    Article  Google Scholar 

  8. Coenegrachts K, Delanote J, Ter Beek L et al (2007) Improved focal liver lesion detection: comparison of single-shot diffusion-weighted echoplanar and single-shot T2 weighted turbo spin echo techniques. Br J Radiol 80:524–531

    Article  CAS  Google Scholar 

  9. Padhani AR, Liu G, Koh DM et al (2009) Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. Neoplasia 11:102–125

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  11. Guiu B, Cercueil JP (2011) Liver diffusion-weighted MR imaging: the tower of Babel? Eur Radiol 21:463–467

    Article  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

    Article  Google Scholar 

  13. Aoyagi T, Shuto K, Okazumi S et al (2012) Apparent diffusion coefficient correlation with oesophageal tumour stroma and angiogenesis. Eur Radiol 22:1172–1177

    Article  Google Scholar 

  14. Cho GY, Kim S, Jensen JH et al (2012) A versatile flow phantom for intravoxel incoherent motion MRI. Magn Reson Med 67:1710–1720

    Article  Google Scholar 

  15. 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 274:405–415. https://doi.org/10.1148/radiol.14140759

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  17. Cohen AD, Schieke MC, Hohenwalter MD, Schmainda KM (2015) The effect of low b-values on the intravoxel incoherent motion derived pseudodiffusion parameter in liver. Magn Reson Med 73:306–311. https://doi.org/10.1002/mrm.25109

    Article  PubMed  Google Scholar 

  18. 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. https://doi.org/10.1371/journal.pone.0125653

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. 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. https://doi.org/10.1371/journal.pone.0113846

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 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

    Article  Google Scholar 

  21. Wang M, Li X, Zou J et al (2016) Evaluation of hepatic tumors using intravoxel incoherent motion diffusion-weighted MRI. Med Sci Monit 22:702–709. https://doi.org/10.12659/MSM.895909

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ichikawa S, Motosugi U, Ichikawa T et al (2013) Intravoxel incoherent motion imaging of focal hepatic lesions. J Magn Reson Imaging 37:1371–1376. https://doi.org/10.1002/jmri.23930

    Article  PubMed  Google Scholar 

  23. Doblas S, Wagner M, Leitao HS et al (2013) Determination of malignancy and characterization of hepatic tumor type with diffusion-weighted magnetic resonance imaging: comparison of apparent diffusion coefficient and intravoxel incoherent motion–derived measurements. Invest Radiol 48:722–728

    Article  Google Scholar 

  24. 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 41:149–156. https://doi.org/10.1002/jmri.24538

    Article  PubMed  Google Scholar 

  25. 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 23:428–434

    Article  CAS  Google Scholar 

  26. Penner A-H, Sprinkart AM, Kukuk GM et al (2013) Intravoxel incoherent motion model-based liver lesion characterisation from three b-value diffusion-weighted MRI. Eur Radiol 23:2773–2783. https://doi.org/10.1007/s00330-013-2869-z

    Article  PubMed  Google Scholar 

  27. Mürtz P, Penner A-H, Pfeiffer A-K et al (2016) Intravoxel incoherent motion model-based analysis of diffusion-weighted magnetic resonance imaging with 3 b-values for response assessment in locoregional therapy of hepatocellular carcinoma. Onco Targets Ther 9:6425–6433. https://doi.org/10.2147/OTT.S113909

    Article  PubMed  PubMed Central  Google Scholar 

  28. Pieper C, Meyer C, Sprinkart AM et al (2016) The value of intravoxel incoherent motion model-based diffusion-weighted imaging for outcome prediction in resin-based radioembolization of breast cancer liver metastases. Onco Targets Ther 9:4089–4098. https://doi.org/10.2147/OTT.S104770

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Pieper CC, Sprinkart AM, Meyer C et al (2016) Evaluation of a simplified intravoxel incoherent motion (IVIM) analysis of diffusion-weighted imaging for prediction of tumor size changes and imaging response in breast cancer liver metastases undergoing radioembolization: a retrospective single center analysis. Medicine 95:e3275. https://doi.org/10.1097/MD.0000000000003275

    Article  PubMed  PubMed Central  Google Scholar 

  30. Pieper CC, Willinek WA, Meyer C et al (2016) Intravoxel incoherent motion diffusion-weighted MR imaging for prediction of early arterial blood flow stasis in radioembolization of breast cancer liver metastases. J Vasc Interv Radiol 27:1320–1328. https://doi.org/10.1016/j.jvir.2016.04.018

    Article  PubMed  Google Scholar 

  31. Bruix J, Sherman M (2011) Management of hepatocellular carcinoma: an update. Hepatology 53:1020–1022

    Article  Google Scholar 

  32. Liu Y, Ye Z, Sun H, Bai R (2013) Grading of uterine cervical cancer by using the ADC difference value and its correlation with microvascular density and vascular endothelial growth factor. Eur Radiol 23:757–765. https://doi.org/10.1007/s00330-012-2657-1

    Article  PubMed  Google Scholar 

  33. Zhu L, Cheng Q, Luo W et al (2015) A comparative study of apparent diffusion coefficient and intravoxel incoherent motion-derived parameters for the characterization of common solid hepatic tumors. Acta Radiol 56:1411–1418. https://doi.org/10.1177/0284185114559426

    Article  PubMed  Google Scholar 

  34. Watanabe H, Kanematsu M, Goshima S et al (2014) Characterizing focal hepatic lesions by free-breathing intravoxel incoherent motion MRI at 3.0 T. Acta Radiol 55:1166–1173. https://doi.org/10.1177/0284185113514966

    Article  PubMed  Google Scholar 

  35. Colagrande S, Regini F, Pasquinelli F et al (2013) Focal liver lesion classification and characterization in noncirrhotic liver: a prospective comparison of diffusion-weighted magnetic resonance–related parameters. J Comput Assist Tomogr 37:560–567

    Article  Google Scholar 

  36. Coenegrachts K (2009) Magnetic resonance imaging of the liver: New imaging strategies for evaluating focal liver lesions. World J Radiol 1:72. https://doi.org/10.4329/wjr.v1.i1.72

    Article  PubMed  PubMed Central  Google Scholar 

  37. Moteki T, Horikoshi H (2006) Evaluation of hepatic lesions and hepatic parenchyma using diffusion-weighted echo-planar MR with three values of gradient b-factor. J Magn Reson Imaging 24:637–645

    Article  Google Scholar 

  38. Lewin M, Fartoux L, Vignaud A et al (2011) The diffusion-weighted imaging perfusion fraction f is a potential marker of sorafenib treatment in advanced hepatocellular carcinoma: a pilot study. Eur Radiol 21:281–290

    Article  Google Scholar 

  39. Kim S, Decarlo L, Cho GY et al (2012) Interstitial fluid pressure correlates with intravoxel incoherent motion imaging metrics in a mouse mammary carcinoma model. NMR Biomed 25:787–794

    Article  Google Scholar 

  40. Lee HJ, Rha SY, Chung YE et al (2014) Tumor perfusion-related parameter of diffusion-weighted magnetic resonance imaging: correlation with histological microvessel density. Magn Reson Med 71:1554–1558. https://doi.org/10.1002/mrm.24810

    Article  PubMed  Google Scholar 

  41. Wagner M, Doblas S, Daire JL et al (2012) Diffusion-weighted MR imaging for the regional characterization of liver tumors. Radiology 264:464–472. https://doi.org/10.1148/radiol.12111530

    Article  PubMed  Google Scholar 

  42. Yoon JH, Lee JM, Yu MH et al (2014) Evaluation of hepatic focal lesions using diffusion-weighted MR imaging: comparison of apparent diffusion coefficient and intravoxel incoherent motion-derived parameters. J Magn Reson Imaging 39:276–285. https://doi.org/10.1002/jmri.24158

    Article  PubMed  Google Scholar 

  43. 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  Google Scholar 

  44. Zhang JL, Sigmund EE, Rusinek H et al (2012) Optimization of b-value sampling for diffusion-weighted imaging of the kidney. Magn Reson Med 67:89–97. https://doi.org/10.1002/mrm.22982

    Article  PubMed  Google Scholar 

  45. Cercueil J-P, Petit J-M, Nougaret S et al (2015) Intravoxel incoherent motion diffusion-weighted imaging in the liver: comparison of mono-, bi- and tri-exponential modelling at 3.0-T. Eur Radiol 25:1541–1550. https://doi.org/10.1007/s00330-014-3554-6

    Article  PubMed  Google Scholar 

Download references

Funding

The authors state that this work has not received any funding.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to P. Mürtz.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Petra Mürtz.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• diagnostic study

• performed at one institution

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mürtz, P., Sprinkart, A.M., Reick, M. et al. Accurate IVIM model-based liver lesion characterisation can be achieved with only three b-value DWI. Eur Radiol 28, 4418–4428 (2018). https://doi.org/10.1007/s00330-018-5401-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-018-5401-7

Keywords

Navigation