Effect of Multipeak Spectral Modeling of Fat for Liver Iron and Fat Quantification: Correlation of Biopsy with MR Imaging Results
Effect of Multipeak Spectral Modeling of Fat for Liver Iron and Fat Quantification: Correlation of Biopsy with MR Imaging Results
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DOI：
10.1148/radiol.12112520
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发表时间：
2012-10-01
期刊：
影响因子：
19.7
通讯作者：
Reeder, Scott B.
中科院分区：
文献类型：
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作者：
Kuehn, Jens-Peter;Hernando, Diego;Reeder, Scott B.
目的：以活检作为参考标准，研究脂肪多峰光谱模型对 R2* 值（作为肝铁测量和肝脏脂肪分数定量）的影响。材料和方法：获得机构审查委员会批准和知情同意。肝病患者（n = 95；50 名男性，45 名女性；平均年龄，57.2 岁 +/- 14.1 [标准差]）接受了非靶向肝活检，并对 97 个活检样本进行了脂肪变性和铁分级检查。活检后 24-72 小时进行 1.5 T MR 成像，使用三回波三维梯度回波序列进行水和脂肪分离。数据离线重建，校正 T1 和 T2* 效应。重建脂肪分数和 R2* 图 (1/T2*)，并使用 Kruskal-Wallis 检验测试有和没有多峰脂肪模型时 R2* 和脂肪变性等级的差异。斯皮尔曼等级相关系数用于评估脂肪分数和脂肪变性等级。进行线性回归分析来比较两种模型的脂肪分数。结果：活检时的平均脂肪变性等级范围为 0% 至 95%。 97 名患者中有 26 名 (27%) 的活检标本显示肝铁（15 名轻度、6 名中度和 5 名重度）。在所有 71 个不含铁的样品中，当使用脂肪单峰建模时，随着脂肪变性等级的增加，观察到表观 R2* 显着增加 (P = .001)。当使用多峰模型时，表观 R2* 作为脂肪变性分级的函数没有差异 (P = .645)，并且 R2* 值与文献中报道的值非常一致。在没有和有光谱建模的情况下，观察到脂肪分数和脂肪变性等级之间存在良好的相关性（r(S) = 0.85）。结论：在存在脂肪的情况下，脂肪的多峰光谱建模提高了 R2* 和肝铁之间的一致性。脂肪的单峰建模会导致肝脏脂肪的低估。 (C) 北美放射学会，2012
Purpose: To investigate the effect of the multipeak spectral modeling of fat on R2* values as measures of liver iron and on the quantification of liver fat fraction, with biopsy as the reference standard.Materials and Methods: Institutional review board approval and informed consent were obtained. Patients with liver disease (n = 95; 50 men, 45 women; mean age, 57.2 years +/- 14.1 [standard deviation]) underwent a nontargeted liver biopsy, and 97 biopsy samples were reviewed for steatosis and iron grades. MR imaging at 1.5 T was performed 24-72 hours after biopsy by using a three-echo three-dimensional gradient-echo sequence for water and fat separation. Data were reconstructed off-line, correcting for T1 and T2* effects. Fat fraction and R2* maps (1/T2*) were reconstructed and differences in R2* and steatosis grades with and without multipeak modeling of fat were tested by using the Kruskal-Wallis test. Spearman rank correlation coefficient was used to assess fat fractions and steatosis grades. Linear regression analysis was performed to compare the fat fraction for both models.Results: Mean steatosis grade at biopsy ranged from 0% to 95%. Biopsy specimens in 26 of 97 patients (27%) showed liver iron (15 mild, six moderate, and five severe). In all 71 samples without iron, a strong increase in the apparent R2* was observed with increasing steatosis grade when single-peak modeling of fat was used (P = .001). When multipeak modeling was used, there were no differences in the apparent R2* as a function of steatosis grading (P = .645), and R2* values agreed closely with those reported in the literature. Good correlation between fat fraction and steatosis grade was observed (r(S) = 0.85) both without and with spectral modeling.Conclusion: In the presence of fat, multipeak spectral modeling of fat improves the agreement between R2* and liver iron. Single-peak modeling of fat leads to underestimation of liver fat. (C) RSNA, 2012