Objective To explore the value of multiple non-gaussian distribution models DWI in evaluating hepatic ischemia reperfusion injury (HIRI) model in rats.Methods Forty-two SD rats were divided into 7 groups by random numeric table method.Each group had 6 rats.The 7 groups were 6 h,12 h,1 d,3 d,7 d,14 d group after HIRI and control group,respectively.The experimental groups underwent right hepatic portal vein and hepatic artery ligation,and received reperfusion 3 h after operation.MRI scanning (T1WI,T2WI,single b-values DWI and 15 b-values DWI) was performed at 6 h,12 h,1 d,3 d,7 d and 14 d after reperfusion.The control group underwent sham operation and MRI scanning.According to monoexponential model,biexponential model,threxponential model,stretched-exponential model DWI and diffusion kurtosis imaging,many parametres were obtained and their dynamic changes at each time point were observed.The parameters included standard apparent diffusion coefficient (ADCs),pure diffusion coefficients (D),pseudodiffusion coefficients (D*),perfusion fraction (f),ultra-high apparent diffusion coefficient (ADCu),distributed diffusion coefficient (DDC),water diffusion heterogeneity index (or),mean diffusion coefficient (MD) and mean diffusion kurtosis (MK).One way ANOVA was used to compare the differences of parameters among different groups.Results MRI examination and pathological examination were successfully completed in all rats.The right hepatic lobe in the experimental groups appeared hypointense on T1WI and hyperintense on T2WI relative to control group from 6 h after operation.The infarcted liver lobe in the experimental groups became significantly smaller at 1 week after HIRI and almost disappeared at 2 weeks after HIRI.The findings of DWI at different b-values were consistent with those at T2WI.There were significant differences in parameters among 6 h,12 h,1 d,3 d,7 d,14 d groups after HIRI and control group (P＜0.05).There were significant differences among 12 h after HIRI,1 d after HIRI,3 d after HIRI and 6 h after HIRI,7 d after HIRI,14 d after HIRI,control group concerning ADCs values respectively (P＜0.05).There were significant differences among 6 h after HIRI,12 h after HIRI,1 d after HIRI,3 d after HIRI,7 d after HIRI and 14 d after HIRI,control group concerning D,D*,f,ADCu,α,DDC,MK values respectively (P＜0.05).In addition,there were significant differences among 1 d after HIRI,3 d after HIRI and 6 h after HIRI,12 h after HIRI,7 d after HIRI and 14 d after HIRI,control group concerning D values respectively (P＜0.05).There were significant differences among 6 h after HIRI,12 h after HIRI,1 d after HIRI,3 d after HIRI and 7 d after HIRI,14 d after HIRI,control group concerning f values respectively (P＜0.05).There were significant differences among 12 h after HIRI and 14 d after HIRI,control group concerning MD values respectively (P＜0.05).There were significant differences between 1 d after HIRI and 14 d after HIRI concerning MD values (P＜0.05).Conclusion Multiple non-gaussian distribution models DWI is superior to conventional DWI in evaluating HIRI model in rats.

OBJECTIVETo investigate the feasibility of labeling endothelial progenitor cells (EPCs) with a novel dual modal contrast agent Molday IONEverGreen(MIEG) and its performanceMRI.

METHODSEPCs were isolated from rat bone marrow and labeled with 10, 20, 50 μg/mL MIEG, respectively. The labeling rates were identified by Prussian blue staining and fluorescence microscopy. The vitality of EPCs labeled with 20 μg/mL MIEG was detected by trypan blue exclusion test at 1 d, 1 w, 2 w, and 6 w after labeling. EPCs labeled with different concentrations of MIEG were scanned by 3.0T MRI with Tweighted and Tweighted imaging.

RESULTSThe labeling rates for EPCs labeled with different concentrations of MIEG were greater than 98%,and the cytoplasm of labeled EPCs was present with Prussian blue staining. Although the green lighting level went down, the labeling rate at 6 w after labeling was greater than 90%. Trypan blue exclusion test showed that there was no significant difference in the vitality between EPCs labeled with MIEG at 1 d, 1 w, 2 w and 6 w after labeling and EPCs without labeling (all>0.05). There was no difference in signal intensity on Tweighted image among EPCs labeled with different concentrations of MIEG. However, the signal intensity on Tweighted image was reduced in all labeled groups, and the signal reduction became more apparent with increased concentration of MIEG.

CONCLUSIONSEPCs can be effectively labeled by MIEG without interference on the cell viability at the labeled concentration of 20 μg/mL. The signal intensity change of labeled cells can be detected sensitively by Tweighted imaging at 3.0T MRI.

Objective To investigate the value of support vector machine based MRI-radiomics in the differential diagnosis of primary hepatic carcinomas (PHCs). Methods PHCs patients were retrospectively collected from July 2013 to February 2017 in the First Affiliated Hospital of Zhejiang University.All patients underwent unenhanced and enhanced MRI liver scan before surgery,and confirmed by pathology. A total of 294 PHCs patients (305 lesions), including 96 cases (97 lesions) of massive type cholangiocarcinoma (mCC), 107(107 lesions)of hepatocellular carcinoma (HCC), and 91 (101 lesions) of mixed hepatocellular and cholangiocellular carcinomas(HCC-CC).All patients underwent unenhanced and dynamic enhanced MRI liver scan including arterial, portal venous and equilibrium phases. Two hundred and three lesions (65 mCC, 71 HCC, 67 HCC-CC) were assigned into the training set, the remaining 102 lesions(32 mCC,36 HCC,34 HCC-CC)into the validation set,according to a ratio of 2:1.The entire lesions were delineated manually using a region of interest on equilibrium phase of enhanced MRI by using a home-made dedicated software(Analysis Kit,AK,General Electrics,US).Then the least absolute shrinkage and selection operator (LASSO) regression was used to select image features with a method of 10 fold cross-validation, and to reduce the dimensionality. The spearman method was used afterwards to condense the image features by removing redundant.A predictive model of diagnosing the different types of PHCs was established based on support vector machines(SVM),and the accuracy of applying the model in the data sets was used to evaluate the diagnostic efficacy of the model. Results A total of 280 quantitative imaging features were extracted in the training set.Thirty one imaging features were selected after LASSO regression and dimensionality reduction,and 21 features were remained after redundancy removing.The SVM showed the best generalization ability when the first 11 imaging features were used due to the Hughes effect.The 11 imaging features include 4 parameters of histogram,2 of textures,4 of gray-level co-occurrence matrix and 1 of gray-level run length matrix. A predictive model for PHCs was established after the study of the 11 imaging features and a regression analysis using SVM.The accuracy of the predictive model was 80.3% (163/203) in differentiating PHCs in the training set. The accuracy of the model was 75.5% (77/102) after applying it in the validation set. The diagnostic accuracy for HCC-CC, HCC and mCC was 85.3% (29/34), 77.8% (28/36) and 62.5% (20/32), respectively, in the validation set. For HCC-CC, 3 cases were misdiagnosed as mCC and 2 cases as HCC.For HCC,3 cases were misdiagnosed as HCC-CC and 5 cases as mCC.For mCC,9 cases were misdiagnosed as HCC-CC and 3 cases as HCC.The model showed the highest accuracy in predicting HCC-CC.Conclusion Radiomics method based on SVM may have a high accuracy in predicting different pathologic types of PHC,with the highest accuracy for HCC-CC.