摘要目的：探讨采用基于最小二乘法估计和不对称回波迭代分解水和脂肪的脂肪成像（IDEAL-IQ）序列定量评估水-脂模型中脂肪含量的可行性和准确性。方法将脂肪乳注射液和蒸馏水混合，配成脂肪含量分别为0.00、0.01、0.02、0.04、0.06、0.08、0.10、0.14、0.18、0.22、0.26、0.30 g/ml的水-脂混合液模型；将凡士林上层注入蒸馏水制作水-凡士林分层模型。对上述模型行IDEAL-IQ序列扫描，其中水-脂混合液模型采用薄层扫描方式，间隔3d后再次扫描一次；水-凡士林分层模型采集层厚为14 mm，反复采集19次，每次扫描后定位扫描框向尾端移动1 mm。水-脂混合液模型图像中选取包含模型的中间11层放置ROI，测得脂肪分数；水-凡士林分层模型图像在FatFrac系列图像中选择包含水-凡士林界面的层面，在模型的中央区放置ROI，测得脂肪分数。采用单样本t检验或单样本Kolmogorov-Sirmov检验比较IDEAL-IQ序列扫描测得的脂肪分数和实际脂肪含量的差异，采用独立样本t检验比较水-脂混合液模型同浓度第1次扫描和第2次扫描测得的脂肪分数的差异，用简单线性回归方法分析脂肪分数与实际脂肪含量的线性关系，采用回归曲线估计分析脂肪分数与定位位置的关系。结果（1）水-脂混合液模型：第1次IDEAL-IQ序列扫描，实际脂肪含量为0.00、0.02、0.06、0.08 g/ml时，测得的脂肪分数为0.60%、（2.30±0.60）%、（5.76±1.40）%、（7.62±1.40）%，差异无统计学意义（P均>0.05）；第2次扫描实际脂肪含量为0.00、0.02、0.10 g/ml时，测得的脂肪分数为0.04%、（2.32±0.60）%、（9.41±1.00）%，差异无统计学意义（P均>0.05）。第1次和第2测得的脂肪分数差异均无统计学意义（P均>0.05）。IDEAL-IQ序列测量的脂肪含量与实际浓度间呈线性关系，回归方程为Y=0.898X+0.224，r2=0.993，P<0.01，F值=36129.548。（2）水-凡士林分层模型：随层面位置改变，测得的脂肪分数升高，呈二次曲线关系，回归方程为Y=0.045X2－0.499X－4.474，r2=0.978，P<0.05，F值=350.623。结论 IDEAL-IQ序列无创测量脂肪含量具有可行性，具有较好的可重复性，利用线性方程能较准确评估实际脂肪含量。

abstractsObjective To validate the feasibility and accuracy of iterative decomposition of water and fat with asymmetry and least squares estimation-quantitative fat imaging (IDEAL-IQ) in fat quantification using fat-water model. Methods A homogeneous fat-water mixture model consisting of various known fat-fractions were described, and the fat fraction was 0.00, 0.01, 0.02, 0.04, 0.06, 0.08, 0.10, 0.14, 0.18, 0.22, 0.26, 0.30 g/ml respectively. A water-vaseline separated model was also described. IDEAL-IQ was performed. Thin slices were acquired for fat-water mixture model and repeated 3 days later. Nineteen slices of 14 mm-thick parallel to the water-vaseline boundary in 1 mm steps from vaseline to water <br> were acquired. The fat-fractions in 11 slices of fat-water mixture model were measured on FatFrac images. Accuracy was assessed through single sample t test or Kolmogorov-Sirmov test. Measured fat-fractions of the same known fat-fraction were assessed through independent samples t test between two scan times. Linear regression was used to assess the relationship between known fat-fractions and measured fat-fractions. Slices containing the water-vaseline boundary were measured with ROI in the middle of the FatFrac images. The relationship between measured fat-fractions and locations of scanning was exploded using curve fitting. Results (1) Fat-water mixture model: no significant difference(P>0.05) was found between measured fat-fractions and known fat-fractions when it was 0.00, 0.02, 0.06 and 0.08 g/ml with the measured fat-fractions 0.60%, (2.30 ± 0.60)%, (5.76 ± 1.40)%, (7.62 ± 1.40)% respectively for the first time. No significant difference(P>0.05) was found between measured fat-fractions and known fat-fractions when it was 0.00, 0.02, 0.10 g/ml with the measured fat-fractions 0.04%, (2.32 ± 0.60)%, (9.41 ± 1.00)%respectively for the second time. Measured fat-fraction was inlinear relation with known fat-fraction:Y=0.898X+0.224, r2=0.993, P<0.01, F=36 129.548.(2) Water-vaseline separated model: measured fat-fraction increased as scanning location changed, Y=0.045X2-0.499X-4.474, r2=0.978, P<0.05, F=350.623.Conclusions IDEAL-IQ can be used to quantify fat content with good repeatability and can accurately assess the actual fat content from the linearrelationship.