中华放射学杂志
中華放射學雜誌
중화방사학잡지
Chinese Journal of Radiology
2010年
9期
954-957
,共4页
徐莉%梁长虹%萧远球%张忠林
徐莉%樑長虹%蕭遠毬%張忠林
서리%량장홍%소원구%장충림
肝%磁共振波谱学
肝%磁共振波譜學
간%자공진파보학
Liver%Magnetic resonance spectroscopy
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.
目的 採用在體3.0 T MRS方法篩選齣影響肝髒脂質沉積的重要因素併建立迴歸預測方程.方法 正常及瀰漫性脂肪肝誌願者共44名,記錄身高、年齡、體質量及體質量指數(BMI),採用3.0 T超導MR掃描儀,8通道腹部相控陣線圈,行單體素點分辨波譜分析法序列採集.採集前行常規預掃描,記錄線寬(LW)及抑水率(WS),所穫譜線採用後處理軟件分析.以水為內參照,對肝髒的脂肪含量進行標準化,定義為脂(%)=脂/(脂+水)×100%.對身高、體質量、年齡、BMI、LW、WS和肝髒標準化脂質含量採用Person方法進行相關分析,以肝髒中脂質含量為因變量進行多元線性迴歸,採用逐步迴歸方法建立預測方程.結果 受試者肝髒脂質含量(0.00～0.96%,中位數為0.02%)與年齡[(39.1±12.6)歲]、體質量[(64.4±10.4)kg]、BMI(23.3±3.1)、LW(18.9±4.4)及WS[(90.7±6.5)%]5箇因素的錶達有相關性(r值分彆為0.11、0.44、0.40、0.52和-0.73,P值均<0.05),隻有年齡、BMI、LW及WS進入多元線性迴歸方程.預測方程標準化脂質含量(Y)=1.395-(0.021×WS)+(0.022×BMI)+(0.014×LW)-(0.004×年齡),決定繫數為0.61,校正決定繫數為0.59.結論 該迴歸模型擬閤較好,自變量年齡、BMI、LW及WS能夠解釋約60%肝髒脂質含量的變化.
목적 채용재체3.0 T MRS방법사선출영향간장지질침적적중요인소병건립회귀예측방정.방법 정상급미만성지방간지원자공44명,기록신고、년령、체질량급체질량지수(BMI),채용3.0 T초도MR소묘의,8통도복부상공진선권,행단체소점분변파보분석법서렬채집.채집전행상규예소묘,기록선관(LW)급억수솔(WS),소획보선채용후처리연건분석.이수위내삼조,대간장적지방함량진행표준화,정의위지(%)=지/(지+수)×100%.대신고、체질량、년령、BMI、LW、WS화간장표준화지질함량채용Person방법진행상관분석,이간장중지질함량위인변량진행다원선성회귀,채용축보회귀방법건립예측방정.결과 수시자간장지질함량(0.00～0.96%,중위수위0.02%)여년령[(39.1±12.6)세]、체질량[(64.4±10.4)kg]、BMI(23.3±3.1)、LW(18.9±4.4)급WS[(90.7±6.5)%]5개인소적표체유상관성(r치분별위0.11、0.44、0.40、0.52화-0.73,P치균<0.05),지유년령、BMI、LW급WS진입다원선성회귀방정.예측방정표준화지질함량(Y)=1.395-(0.021×WS)+(0.022×BMI)+(0.014×LW)-(0.004×년령),결정계수위0.61,교정결정계수위0.59.결론 해회귀모형의합교호,자변량년령、BMI、LW급WS능구해석약60%간장지질함량적변화.
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.
