Chronic liver disease: The role of multiple diffusion-weighted models using the Bayesian shrinkage method for liver fibrosis assessment - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Chronic liver disease: The role of multiple diffusion-weighted models using the Bayesian shrinkage method for liver fibrosis assessment

Résumé

Liver fibrosis is one of the leading features in chronic liver disease (CLD) since it conditions the prognosis and guides the treatment strategy. In this work, estimated parameters from various diffusion-weighted MRI models fitted by the Bayesian method were analyzed for the relationship with liver fibrosis through spearman's correlation and t-test. Four parameters (D , σ, D _F, D) were selected for fibrosis classification and achieved the best result based on the decision tree. Our result suggested that the statistical model and a hybrid IVIM-DKI model are promising models and confirmed the confounding effect of fat for diffusivity to assess liver fibrosis.

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Dates et versions

hal-04211967 , version 1 (20-09-2023)

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  • HAL Id : hal-04211967 , version 1

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Jiqing Huang, Benjamin Leporq, Olivier Beuf, Helene Ratiney. Chronic liver disease: The role of multiple diffusion-weighted models using the Bayesian shrinkage method for liver fibrosis assessment. ISMRM, Jun 2023, Toronto, Canada. ⟨hal-04211967⟩
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