Related Experiment Video
Updated: Jul 29, 2025
Author Spotlight: A Non-Invasive Tool to Assess and Differentiate Fat Patterns in Liver Using 3D Dixon MRI
Published on: October 20, 2023
Diffusion-Weighted MRI of the Liver in Patients With Chronic Liver Disease: A Comparative Study Between Different
Jiqing Huang1, Benjamin Leporq1, Valérie Hervieu2
1Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, CNRS, Inserm, CREATIS UMR 5220, U1294, Lyon, France.
Background:
Diffusion-weighted imaging (DWI) has been considered for chronic liver disease (CLD) characterization. Grading of liver fibrosis is important for disease management.
Purpose:
To investigate the relationship between DWI's parameters and CLD-related features (particularly regarding fibrosis assessment).
Study Type:
Retrospective.
Subjects:
Eighty-five patients with CLD (age: 47.9 ± 15.5, 42.4% females).
Field Strength/Sequence:
3-T, spin echo-echo planar imaging (SE-EPI) with 12 b-values (0-800 s/mm2 ).
Assessment:
Several models statistical models, stretched exponential model, and intravoxel incoherent motion were simulated. The corresponding parameters (Ds , σ, DDC, α, f, D, D*) were estimated on simulation and in vivo data using the nonlinear least squares (NLS), segmented NLS, and Bayesian methods. The fitting accuracy was analyzed on simulated Rician noised DWI. In vivo, the parameters were averaged from five central slices entire liver to compare correlations with histological features (inflammation, fibrosis, and steatosis). Then, the differences between mild (F0-F2) or severe (F3-F6) groups were compared respecting to statistics and classification. A total of 75.3% of patients used to build various classifiers (stratified split strategy and 10-folders cross-validation) and the remaining for testing.
Statistical Tests:
Mean squared error, mean average percentage error, spearman correlation, Mann-Whitney U-test, receiver operating characteristic (ROC) curve, area under ROC curve (AUC), sensitivity, specificity, accuracy, precision. A P-value <0.05 was considered statistically significant.
Results:
In simulation, the Bayesian method provided the most accurate parameters. In vivo, the highest negative significant correlation (Ds , steatosis: r = -0.46, D*, fibrosis: r = -0.24) and significant differences (Ds , σ, D*, f) were observed for Bayesian fitted parameters. Fibrosis classification was performed with an AUC of 0.92 (0.91 sensitivity and 0.70 specificity) with the aforementioned diffusion parameters based on the decision tree method.
Data Conclusion:
These results indicate that Bayesian fitted parameters may provide a noninvasive evaluation of fibrosis with decision tree.
Evidence Level:
1 TECHNICAL EFFICACY: Stage 1.
Insights
Diffusion-weighted imaging (DWI) parameters, particularly those fitted using the Bayesian method, show significant correlations with liver fibrosis. This approach offers a noninvasive method for assessing fibrosis in chronic liver disease (CLD).
Area of Science:
- Medical Imaging
- Radiology
- Biophysics
Background:
- Diffusion-weighted imaging (DWI) is explored for characterizing chronic liver disease (CLD).
- Accurate grading of liver fibrosis is crucial for effective CLD management.
Purpose of the Study:
- To determine the relationship between DWI parameters and CLD features, with a focus on fibrosis assessment.
- To evaluate the efficacy of various modeling techniques for DWI data analysis in CLD.
Main Methods:
- Retrospective analysis of 85 CLD patients using 3-T DWI with 12 b-values.
- Application of statistical models, stretched exponential model, and intravoxel incoherent motion models.
- Estimation of parameters (Ds, σ, DDC, α, f, D, D*) using nonlinear least squares and Bayesian methods; comparison with histological features.
Main Results:
- The Bayesian method demonstrated superior accuracy in parameter estimation compared to other models.
- Significant correlations were found between Bayesian-fitted parameters (Ds, D*) and liver steatosis/fibrosis.
- A decision tree classifier using these parameters achieved an AUC of 0.92 for fibrosis classification (0.91 sensitivity, 0.70 specificity).
Conclusions:
- Bayesian-fitted DWI parameters offer a promising noninvasive approach for evaluating liver fibrosis in CLD.
- The decision tree method, utilizing these parameters, provides an effective tool for fibrosis staging.
More Related Videos
Related Concept Videos
00:57Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models
01:17Assessment of Diffusion and Perfusion
The Role of Diffusion in Respiration
Diffusion is the process by which molecules move from an area of higher concentration to an area of lower concentration. In the respiratory system, this...
01:24Magnetic Resonance Imaging