Sanz-Requena Roberto, Prats-Montalbán José Manuel, Martí-Bonmatí Luis, Alberich-Bayarri Ángel, García-Martí Gracián, Pérez Rosario, Ferrer Alberto
Biomedical Engineering, Hospital Quirón Valencia, Valencia, Spain.
GIBI230, Hospital Universitari i Politècnic La Fe, Valencia, Spain.
J Magn Reson Imaging. 2015 Aug;42(2):477-87. doi: 10.1002/jmri.24805. Epub 2014 Nov 20.
To introduce a segmentation method to calculate an automatic arterial input function (AIF) based on principal component analysis (PCA) of dynamic contrast enhanced MR (DCE-MR) imaging and compare it with individual manually selected and population-averaged AIFs using calculated pharmacokinetic parameters.
The study included 65 individuals with prostate examinations (27 tumors and 38 controls). Manual AIFs were individually extracted and also averaged to obtain a population AIF. Automatic AIFs were individually obtained by applying PCA to volumetric DCE-MR imaging data and finding the highest correlation of the PCs with a reference AIF. Variability was assessed using coefficients of variation and repeated measures tests. The different AIFs were used as inputs to the pharmacokinetic model and correlation coefficients, Bland-Altman plots and analysis of variance tests were obtained to compare the results.
Automatic PCA-based AIFs were successfully extracted in all cases. The manual and PCA-based AIFs showed good correlation (r between pharmacokinetic parameters ranging from 0.74 to 0.95), with differences below the manual individual variability (RMSCV up to 27.3%). The population-averaged AIF showed larger differences (r from 0.30 to 0.61).
The automatic PCA-based approach minimizes the variability associated to obtaining individual volume-based AIFs in DCE-MR studies of the prostate.
介绍一种基于动态对比增强磁共振（DCE-MR）成像主成分分析（PCA）来计算自动动脉输入函数（AIF）的分割方法，并使用计算得到的药代动力学参数将其与个体手动选择的AIF和群体平均AIF进行比较。
该研究纳入了65名接受前列腺检查的个体（27例肿瘤患者和38例对照）。分别提取个体手动AIF并进行平均以获得群体AIF。通过将PCA应用于容积DCE-MR成像数据并找到主成分与参考AIF的最高相关性来分别获得自动AIF。使用变异系数和重复测量检验评估变异性。将不同的AIF用作药代动力学模型的输入，并获得相关系数、Bland-Altman图和方差分析检验以比较结果。
在所有病例中均成功提取了基于PCA的自动AIF。手动AIF和基于PCA的AIF显示出良好的相关性（药代动力学参数之间的r值范围为0.74至0.95），差异低于手动个体变异性（RMSCV高达27.3%）。群体平均AIF显示出更大的差异（r值范围为0.30至0.61）。
在前列腺的DCE-MR研究中，基于PCA的自动方法可将与获取基于个体容积的AIF相关的变异性降至最低。
