Liver fibrosis: stretched exponential model outperforms mono-exponential and bi-exponential models of diffusion-weighted MRI
- 304 Downloads
To compare the ability of diffusion-weighted imaging (DWI) parameters acquired from three different models for the diagnosis of hepatic fibrosis (HF).
Ninety-five patients underwent DWI using nine b values at 3 T magnetic resonance. The hepatic apparent diffusion coefficient (ADC) from a mono-exponential model, the true diffusion coefficient (D t ), pseudo-diffusion coefficient (D p ) and perfusion fraction (f) from a biexponential model, and the distributed diffusion coefficient (DDC) and intravoxel heterogeneity index (α) from a stretched exponential model were compared with the pathological HF stage. For the stretched exponential model, parameters were also obtained using a dataset of six b values (DDC#, α#). The diagnostic performances of the parameters for HF staging were evaluated with Obuchowski measures and receiver operating characteristics (ROC) analysis. The measurement variability of DWI parameters was evaluated using the coefficient of variation (CoV).
Diagnostic accuracy for HF staging was highest for DDC# (Obuchowski measures, 0.770 ± 0.03), and it was significantly higher than that of ADC (0.597 ± 0.05, p < 0.001), D t (0.575 ± 0.05, p < 0.001) and f (0.669 ± 0.04, p = 0.035). The parameters from stretched exponential DWI and D p showed higher areas under the ROC curve (AUCs) for determining significant fibrosis (≥F2) and cirrhosis (F = 4) than other parameters. However, D p showed significantly higher measurement variability (CoV, 74.6%) than DDC# (16.1%, p < 0.001) and α# (15.1%, p < 0.001).
Stretched exponential DWI is a promising method for HF staging with good diagnostic performance and fewer b-value acquisitions, allowing shorter acquisition time.
• Stretched exponential DWI provides a precise and accurate model for HF staging.
• Stretched exponential DWI parameters are more reliable than D p from bi-exponential DWI model
• Acquisition of six b values is sufficient to obtain accurate DDC and α
KeywordsLiver Fibrosis Liver cirrhosis Diffusion magnetic resonance imaging
Alpha, Intravoxel heterogeneity index
α obtained using a six-b-value dataset (in this study)
Coefficient of variation
Distributed diffusion coefficient
DDC obtained using a six-b-value dataset (in this study)
True diffusion coefficient
Intravoxel incoherent motion
The authors state that this work has not received any funding.
Compliance with ethical standards
The scientific guarantor of this publication is Yong Eun Chung.
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
Hyunsoo Yang in Yonsei University Health System performed statistical analysis, and he is not one of the authors.
Written informed consent was waived by the Institutional Review Board.
Institutional Review Board approval was obtained.
• diagnostic study
• performed at one institution
- 7.Poynard T, Munteanu M, Luckina E et al (2013) Liver fibrosis evaluation using real-time shear wave elastography: applicability and diagnostic performance using methods without a gold standard. J Hepatol 58:928–935Google Scholar
- 9.Taouli B, Tolia AJ, Losada M et al (2007) Diffusion-weighted MRI for quantification of liver fibrosis: preliminary experience. AJR Am J Roentgenol 189:799–806Google Scholar
- 12.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 38:110–116Google Scholar
- 13.Chung SR, Lee SS, Kim N et al (2015) Intravoxel incoherent motion MRI for liver fibrosis assessment: a pilot study. Acta Radiol 56:1428–1436Google Scholar
- 16.Bai Y, Lin Y, Tian J et al (2016) Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging. Radiology 278:496–504Google Scholar
- 17.Winfield JM, OrtonMR, Collins DJ et al (2017) Separation of type and grade in cervical tumours using non-mono-exponential models of diffusion-weighted MRI. Eur Radiol 27:627–636Google Scholar
- 18.Orton MR, Messiou C, Collins D et al (2016) Diffusion-weighted MR imaging of metastatic abdominal and pelvic tumours is sensitive to early changes induced by a VEGF inhibitor using alternative diffusion attenuation models. Eur Radiol 26:1412–1419Google Scholar
- 19.Leitao 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 283:98–107Google Scholar
- 21.Zhang Q, Yu NN, Wen LJ et al (2012) A preliminary study of apparent diffusion coefficient in chemotherapy-induced liver damage. Eur J Radiol 81:2943–2946Google Scholar
- 22.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) 42:468–477Google Scholar
- 24.Cho A, Chung YE, Choi JS et al (2017) Feasibility of preoperative FDG PET/CT total hepatic glycolysis in the remnant liver for the prediction of postoperative liver function. AJR Am J Roentgenol 208:624–631Google Scholar
- 25.Luciani A, Vignaud A, Cavet M et al (2008) Liver cirrhosis: intravoxel incoherent motion MR imaging—pilot study. Radiology 249:891–899Google Scholar
- 27.Kwee TC, Galban CJ, Tsien C et al (2010) Intravoxel water diffusion heterogeneity imaging of human high-grade gliomas. NMR Biomed 23:179–187Google Scholar
- 28.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 274:405–415Google Scholar
- 30.Jiang H, Chen J, Gao R, Huang Z, Wu M, Song B (2017) Liver fibrosis staging with diffusion-weighted imaging: a systematic review and meta-analysis. Abdom Radiol (NY) 42:490–501Google Scholar
- 31.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 (NY) 42:1176–1182Google Scholar
- 32.Hennedige TP, Wang G, Leung FP et al (2017) Magnetic resonance elastography and diffusion weighted imaging in the evaluation of hepatic fibrosis in chronic hepatitis B. Gut Liver 11:401–408Google Scholar
- 33.Li YT, Cercueil JP, Yuan J, Chen W, Loffroy R, Wang YX (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 7:59–78CrossRefPubMedPubMedCentralGoogle Scholar
- 34.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 42:204–210Google Scholar
- 35.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 23:428–434Google Scholar
- 36.Jerome NP, Miyazaki K, Collins DJ et al (2017) Repeatability of derived parameters from histograms following non-Gaussian diffusion modelling of diffusion-weighted imaging in a paediatric oncological cohort. Eur Radiol 27:345–353Google Scholar