{"title":"在小鼠模型中利用多室限制频谱磁共振成像分析肝纤维化特征。","authors":"Yeyu Cai, Jiayi Liu, HaiTao Yang, Liyun Zheng, Dongmei Wu, Enhua Xiao, Yongming Dai","doi":"10.1002/mp.17126","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF).</p><p><strong>Purpose: </strong>To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model.</p><p><strong>Methods: </strong>Thirty mice with carbon tetrachloride (CCl<sub>4</sub>)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000 s/mm<sup>2</sup>) 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.</p><p><strong>Results: </strong>According to BIC results, a 4-compartment RSI model (RSI<sub>4</sub>) with optimal ADCs of 0.471 × 10<sup>-3</sup>, 1.653 × 10<sup>-3</sup>, 9.487 × 10<sup>-3</sup>, and > 30 × 10<sup>-3</sup>, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C<sub>1</sub> and C<sub>3</sub> compartments were observed between LF and control groups (P = 0.018 and 0.003, respectively). ROC analysis showed that RSI<sub>4</sub>-C<sub>3</sub> was the most effective single diffusion parameter for characterizing LF (AUC = 0.876, P = 0.003). Furthermore, the combination of ADC values and RSI<sub>4</sub>-C<sub>3</sub> value increased the diagnosis performance significantly (AUC = 0.894, P = 0.002).</p><p><strong>Conclusion: </strong>The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI<sub>4</sub>-C<sub>3</sub> showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI<sub>4</sub>-C<sub>3</sub> values further improved the discrimination performance.</p>","PeriodicalId":94136,"journal":{"name":"Medical physics","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model.\",\"authors\":\"Yeyu Cai, Jiayi Liu, HaiTao Yang, Liyun Zheng, Dongmei Wu, Enhua Xiao, Yongming Dai\",\"doi\":\"10.1002/mp.17126\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Currently, an advanced imaging method may be necessary for magnetic resonance imaging (MRI) to diagnosis and quantify liver fibrosis (LF).</p><p><strong>Purpose: </strong>To evaluate the feasibility of the multicompartmental restriction spectrum imaging (RSI) model to characterize LF in a mouse model.</p><p><strong>Methods: </strong>Thirty mice with carbon tetrachloride (CCl<sub>4</sub>)-induced LF and eight control mice were investigated using multi-b-value (ranging from 0 to 2000 s/mm<sup>2</sup>) 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.</p><p><strong>Results: </strong>According to BIC results, a 4-compartment RSI model (RSI<sub>4</sub>) with optimal ADCs of 0.471 × 10<sup>-3</sup>, 1.653 × 10<sup>-3</sup>, 9.487 × 10<sup>-3</sup>, and > 30 × 10<sup>-3</sup>, was the optimal model to characterize LF. Significant differences in signal contribution fraction of the C<sub>1</sub> and C<sub>3</sub> compartments were observed between LF and control groups (P = 0.018 and 0.003, respectively). ROC analysis showed that RSI<sub>4</sub>-C<sub>3</sub> was the most effective single diffusion parameter for characterizing LF (AUC = 0.876, P = 0.003). Furthermore, the combination of ADC values and RSI<sub>4</sub>-C<sub>3</sub> value increased the diagnosis performance significantly (AUC = 0.894, P = 0.002).</p><p><strong>Conclusion: </strong>The 4-compartment RSI model has the potential to distinguish LF from the control group based on diffusion parameters. RSI<sub>4</sub>-C<sub>3</sub> showed the highest diagnostic performance among all the parameters. The combination of ADC and RSI<sub>4</sub>-C<sub>3</sub> values further improved the discrimination performance.</p>\",\"PeriodicalId\":94136,\"journal\":{\"name\":\"Medical physics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Medical physics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/mp.17126\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Medical physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/mp.17126","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Utilizing multicompartmental restriction spectrum magnetic resonance imaging for liver fibrosis characterization in a mouse model.
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.
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