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

Yeyu Cai; Jiayi Liu; HaiTao Yang; Liyun Zheng; Dongmei Wu; Enhua Xiao; Yongming Dai

doi:10.1002/mp.17126

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

Med Phys. 2024 May 16. doi: 10.1002/mp.17126. Online ahead of print.

Authors

Yeyu Cai¹, Jiayi Liu², HaiTao Yang¹, Liyun Zheng³, Dongmei Wu⁴, Enhua Xiao¹, Yongming Dai⁵

Affiliations

¹ Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China.
² Department of Oncology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, China.
³ Shanghai Institute of Medical Imaging, Shanghai, China.
⁴ Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronics Science, East China Normal University, Shanghai, China.
⁵ School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China.

PMID: 38753987
DOI: 10.1002/mp.17126

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 (CCl₄)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000 s/mm²) 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 (RSI₄) 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 C₁ and C₃ compartments were observed between LF and control groups (P = 0.018 and 0.003, respectively). ROC analysis showed that RSI₄-C₃ was the most effective single diffusion parameter for characterizing LF (AUC = 0.876, P = 0.003). Furthermore, the combination of ADC values and RSI₄-C₃ 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. RSI₄-C₃ showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI₄-C₃ values further improved the discrimination performance.

Keywords: ADC; DWI; MRI; RSI; liver fibrosis.

Grants and funding

The Second Xiangya Hospital of Central South University