Objective To explore the value of single source dual energy CT for quantitative measurement of liver fat fraction in the rabbit model of nonalcoholic fatty liver disease(NAFLD).Methods Thirty male New Zealand rabbits were randomly divided into five groups.Six rabbits were fed with standard chow as a control group for 3 weeks.TwentyGfour rabbits were divided into four groups and fed with highGfat, highGcholesterol diet to reach different stage of NAFLD model for 1 ,3 ,4 and 8 weeks respectively before dualGenergy CT scanning.1 40 keV polychromatic CT values (QC),70 keV monochromatic CT values (Mono 70 keV),slope,effective atomic number (EffectiveGZ)and fat concentration based on dualGenergy CT fat decomposition (Fat/Water)were measured.Liver samples were obtained to measure the fat fraction and staged according to Burnt staging system.Correlations between different CT indexes and fat fraction were analyzed.ROC was used to evaluate the diagnosis efficacy of different parameters.Results Correlation between fat concentration based on dualGenergy CT fat decomposition and fat fraction (r＝0.936)was better than that between 140 keV polychromatic CT values (r＝－0.838)and 70 keV monochromatic CT values (r＝－0.906),as well as effective atomic number (r＝－0.858)and slope (r＝0.863).In terms of diagnostic performance of material decomposition fat imaging,the values of area under the curve were 0.944 (stage 0 vs.stage 1 or more severe),0.995 (stage 1 or less severe vs.stage 2 or more severe)and 1 (stage 2 or less severe vs.stage 3)with optimal cutoff values of 59.310,99.5 17 and 22 3.02 3 mg/cm3 ,respectively.Conclusion The dualGenergy CT can quantitatively measure liver fat concentration as a noninvasive surrogate bioGmarker in the rabbit model of nonalcoholic fatty liver disease.DualGenergy CT derived material decomposition fat images can provide more diagnostic information at the early stage of NAFLD.

Objective To investigate the feasibility of histogram analysis in differentiating brain high-grade glioblastomas,primary lymphoma from metastatic tumor.Methods 26 cases of brain high-grade glioblastomas,22 cases of primary lymphoma and 18 cases of metastatic tumor confirmed by postoperative pathological were analyzed retrospectively.Delineation of ROI and extraction of texture parameters were performing by using Mazda software.The histogram parameters,including Mean,Variance,Skewness,Kurtosis,Perc0.1%,Perc10%,Perc50%, Perc90% and Perc99% were analyzed statistically,and the ROC was then established.Results Mean,Perc0.1%,Perc10%and Perc50% exhibited statistically significant differences (P＜0.05).The best diagnostic parameters for differentiation between brain high-grade glioblastomas and primary lymphoma,primary lymphoma and metastatic tumor,and brain high-grade glioblastomas and metastatic tumor were Perc0.1%,Perc0.1%and Kurtosis.The AUC for these preferred diagnostic parameters were 0.937,0.879 and 0.7 1 8,respectively,with optimal thresholds of 50,70 and －0.43,sensitivity and specificity of 90.9% and 88.5%,77.3% and 88.9%,and 61.5% and 77.8%.Conclusion The histogram analysis of MRI images contributes to differentiate quantitatively between brain high-grade glioblastomas,primary lymphoma and metastatic tumor.