• Roberto Sanz-Requena, José Manuel Prats-Montalbán, Luis Martí-Bonmatí, Ángel Alberich-Bayarri, Gracián García-Martí, Rosario Pérez, and Alberto Ferrer.
• Biomedical Engineering, Hospital Quirón Valencia, Valencia, Spain.
• J Magn Reson Imaging. 2015 Aug 1; 42 (2): 477-87.

BackgroundTo 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.MethodsThe 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.ResultsAutomatic 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).ConclusionThe automatic PCA-based approach minimizes the variability associated to obtaining individual volume-based AIFs in DCE-MR studies of the prostate.© 2014 Wiley Periodicals, Inc.

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