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Updated: Apr 20, 2026

Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
Published on: July 21, 2023
Intravoxel incoherent motion MRI for liver fibrosis assessment: a pilot study
Sae Rom Chung1, Seung Soo Lee2, Namkug Kim1
1Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea.
Background:
There has been a growing need for an imaging method for the accurate diagnosis and staging of liver fibrosis as a non-invasive alternative to liver biopsy.
Purpose:
To evaluate the feasibility of intra-voxel incoherent motion (IVIM) imaging for classifying the severity of liver fibrosis.
Material And Methods:
Fifty-seven patients who underwent navigator-triggered, diffusion-weighted imaging (DWI) of the liver on a 1.5-T system using nine b-values and had a reliable reference standard for the diagnosis of liver fibrosis (histopathologic findings [n = 27] or clinical findings for normal [n = 18] or cirrhotic liver [n = 12]), were included in our study. Liver apparent diffusion coefficient (ADC), pure diffusion (Dslow), perfusion fraction (f), and perfusion-related diffusion (Dfast), and the product f · Dfast were compared with the liver fibrosis stages (F). The accuracies of these parameters in diagnosing severe liver fibrosis (F ≥3) were evaluated using the receiver-operating characteristic (ROC) curve analysis.
Results:
The liver fibrosis stages had the strongest negative correlation with f · Dfast (ρ = -0.52). All of the parameters, except for Dslow, were significantly lower in patients with F ≥3 than in those with F ≤2 (P ≤ 0.001). The area under the ROC curve for diagnosing severe fibrosis was the largest for f · Dfast (0.844) with an overall accuracy of 79.0% (45/57) at the optimal cutoff value and followed by f (0.834), Dfast (0.773), ADC (0.762), and Dslow (0.656).
Conclusion:
IVIM imaging is a promising method for classifying the severity of liver fibrosis, with the product f · Dfast being the most accurate parameter.
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