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• Original Article •
Experimental study on diagnosis of metabolic dysfunction-associated steatohepatitis using different models of multi-b-value diffusion-weighted magnetic resonance imaging
ZHANG Yutong  XIE Shuangshuang  DU Xinzhe  WANG Xuyang  QIN Jiaming  YANG Jiaqi  SHEN Wen 

DOI:10.12015/issn.1674-8034.2025.11.025.


[Abstract] Objective To investigate the diagnostic efficacy of multi-b-value diffusion-weighted imaging (DWI) based on six diffusion models for metabolic dysfunction-associated steatohepatitis (MASH).Materials and Methods Thirty sprague-dawley (SD) rats were randomly divided into three groups (10 rats each) using a random number table: normal control group, metabolic-associated fatty liver (MAFL) group, and MASH group. The MAFL and MASH groups were modeled by feeding a high-fat diet for 10 weeks and 14 weeks, respectively. After modeling, all rats underwent liver multi-b-value DWI. Six models were used to process the data and obtain quantitative parameters of liver parenchyma: mono-exponential model, intravoxel incoherent motion (IVIM) model, diffusion kurtosis imaging (DKI) model, stretched-exponential model (SEM), fractional order calculus (FROC) model, and continuous-time random walk (CTRW) model. The mono-exponential model parameters included apparent diffusion coefficient (ADC), the IVIM model parameters included pure diffusion coefficient (IVIM_D), pseudo-diffusion coefficient (IVIM_D*), perfusion fraction (IVIM_f), the DKI model parameters included mean diffusion coefficient (DKI_MD), mean kurtosis coefficient (DKI_MK), the SEM model parameters included distributed diffusion coefficient (SEM_DDC), heterogeneity index (SEM_α), the FROC model parameters included diffusion coefficient (FROC_D), spatial parameter (FROC_μ), the CTRW model parameters included anomalous diffusion coefficient (CTRW_D), spatial diffusion heterogeneity index (CTRW_β) and temporal diffusion heterogeneity index (CTRW_α). For the single-exponential model, ADC1 was obtained using conventional two b-values, and ADC2 was obtained using multi-b-values. Immediately after MRI examination, the rats were euthanized, and liver specimens were collected for pathological analysis to obtain the nonalcoholic fatty liver disease (NAFLD) activity score (NAS). One-way analysis of variance (ANOVA) or Kruskal-Wallis test was used to compare parameter differences among groups. Spearman rank correlation analysis was used to explore the correlation between MRI quantitative parameters and NAS. The diagnostic efficacy of each parameter for MASH was analyzed using the receiver operating characteristic (ROC) curve.Results The quantitative parameters of liver parenchyma, including ADC2, IVIM_D, DKI_MD, DKI_MK, SEM_DDC, FROC_D, CTRW_D, and CTRW_α, showed statistically significant differences between any two groups (P<0.05). ADC1, SEM_α and FROC_β only differed between the normal group and MASH group (P < 0.05). ADC1, ADC2, IVIM_D, DKI_MD, SEM_DDC, SEM_α, FROC_D, FROC_β, CTRW_D, and CTRW_α were negatively correlated with NAS (r = -0.479 to -0.886), while IVIM_f and DKI_MK were positively correlated with NAS (r = 0.460, 0.860). ROC curve analysis showed that ADC2, IVIM_D, DKI_MD, DKI_MK, SEM_DDC, SEM_α, FROC_D, FROC_β, CTRW_D, and CTRW_α had moderate to high diagnostic efficacy for MASH (area under the curve: 0.780 to 0.960). Among them, ADC2, DKI_MK, and FROC_D were significantly superior to SEM_α and FROC_β (P < 0.05).Conclusions Multiple diffusion models can be used for MASH diagnosis, with the ADC value from the multi-b-value mono-exponential model, MK value from the DKI model, and D value from the FROC model demonstrating the best efficacy and are expected to become the best parameters for non-invasive diagnosis of MASH as an alternative to liver biopsy.
[Keywords] metabolic dysfunction-associated fatty liver；metabolic dysfunction-associated steatohepatitis；magnetic resonance imaging；diffusion weighted imaging；multi-b-value

ZHANG Yutong1, 2   XIE Shuangshuang2   DU Xinzhe2, 3   WANG Xuyang3   QIN Jiaming2, 3   YANG Jiaqi1, 2   SHEN Wen2*  

1 Department of Radiology, First Central Hospital of Tianjin Medical University, Tianjin 300192, China

2 Department of Radiology, Tianjin First Central Hospital, Tianjin Institute of Imaging Medicine, Tianjin 300192, China

3 The School of Medicine, Nankai University, Tianjin 300071, China.

Corresponding author: SHEN W, E-mail: shenwen66happy@126.com

Conflicts of interest   None.

Received  2025-07-08
Accepted  2025-11-10
DOI: 10.12015/issn.1674-8034.2025.11.025
DOI:10.12015/issn.1674-8034.2025.11.025.

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