Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model

Abstract Background Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF). Purpose To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model. Methods Thirty mice with carbon tetrachloride (CCl 4 )‐induced LF and eight control mice were investigated using multi‐ b ‐value (ranging from 0 to 2000 s/mm 2 ) diffusion‐weighted imaging (DWI) on a 3T scanner. DWI data were processed using RSI model (2–5 compartments) with the Bayesian Information Criterion (BIC) determining the optimal model. Conventional ADC value and signal fraction of each compartment in the optimal RSI model were compared across groups. Receiver operating characteristics (ROC) curve analysis was performed to determine the diagnosis performances of different parameters, while Spearman correlation analysis was employed to investigate the correlation between different tissue compartments and the stage of LF. Results According to BIC results, a 4‐compartment RSI model (RSI 4 ) with optimal ADCs of 0.471 × 10 −3 , 1.653 × 10 −3 , 9.487 × 10 −3 , and > 30 × 10 −3 , was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C 1 and C 3 compartments were observed between LF and control groups ( P = 0.018 and 0.003, respectively). ROC analysis showed that RSI 4 ‐C 3 was the most effective single diffusion parameter for characterizing LF (AUC = 0.876, P = 0.003). Furthermore, the combination of ADC values and RSI 4 ‐ C 3 value increased the diagnosis performance significantly (AUC = 0.894, P = 0.002). Conclusion The 4‐compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI 4 ‐C 3 showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI 4 ‐C 3 values further improved the discrimination performance.