中文摘要：

目的 采用在体3.0 T MRS方法筛选出影响肝脏脂质沉积的重要因素并建立回归预测方程.方法 正常及弥漫性脂肪肝志愿者共44名,记录身高、年龄、体质量及体质量指数（BMI）,采用3.0 T超导MR扫描仪,8通道腹部相控阵线圈,行单体素点分辨波谱分析法序列采集.采集前行常规预扫描,记录线宽（LW）及抑水率（WS）,所获谱线采用后处理软件分析.以水为内参照,对肝脏的脂肪含量进行标准化,定义为脂（%）=脂/（脂＋水）×100%.对身高、体质量、年龄、BMI、LW、WS和肝脏标准化脂质含量采用Person方法进行相关分析,以肝脏中脂质含量为因变量进行多元线性回归,采用逐步回归方法建立预测方程.结果 受试者肝脏脂质含量（0.00～0.96%,中位数为0.02%）与年龄[（39.1±12.6）岁]、体质量[（64.4±10.4）kg]、BMI（23.3±3.1）、LW（18.9±4.4）及WS[（90.7±6.5）%]5个因素的表达有相关性（r值分别为0.11、0.44、0.40、0.52和-0.73,P值均〈0.05）,只有年龄、BMI、LW及WS进入多元线性回归方程.预测方程标准化脂质含量（Y）=1.395-（0.021×WS）＋（0.022×BMI）＋（0.014×LW）-（0.004×年龄）,决定系数为0.61,校正决定系数为0.59.结论 该回归模型拟合较好,自变量年龄、BMI、LW及WS能够解释约60%肝脏脂质含量的变化.

英文摘要：

Objective To analyze the correlations between liver lipid level determined by liver 3.0 T 1H-MRS in vivo and influencing factors using multiple linear stepwise regression. Methods The prospective study of liver 1H-MRS was performed with 3.0 T system and eight-channel torso phased-array coils using PRESS sequence. Forty-four volunteers were enrolled in this study. Liver spectra were collected with a TR of 1500 ms ,TE of 30 ms, volume of interest of 2 cm ×2 cm ×2 cm, NSA of 64 times. The acquired raw proton MRS data were processed by using a software program SAGE. For each MRS measurement, using water as the internal reference, the amplitude of the lipid signal was normalized to the sum of the signal from lipid and water to obtain percentage lipid within the liver. The statistical description of height, weight, age and BMI, Line width and water suppression were recorded, and Pearson analysis was applied to test their relationships. Multiple linear stepwise regression was used to set the statistical model for the prediction of Liver lipid content. Results Age （39.1 ± 12. 6） years, body weight （64.4 ± 10. 4） kg,BMI （23.3 ±3.1） kg/m2, linewidth （18.9 ±4.4） and the water suppression （90.7 ±6.5）% had significant correlation with liver lipid content （0.00 to 0.96%, median 0. 02% ）, r were 0.11,0. 44,0. 40,0. 52, - 0. 73 respectively（P 〈 0. 05 ）. But only age, BMI, line width, and the water suppression entered into the multiple linear regression equation. Liver lipid content prediction equation was as follows： Y =1.395-（0.021 × water suppression） ＋ （0.022 × BMI） ＋ （0.014 × line width） - （ 0. 064 × age）,and the coefficient of determination was 0.613, corrected coefficient of determination was 0.59. Conclusion The regression model fitted well, since the variables of age, BMI, width, and water suppression can explain about 60% of liver lipid content changes.