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Automatic individual arterial input functions calculated from PCA outperform manual and population‐averaged approaches for the pharmacokinetic modeling of DCE‐MR images
Author(s) -
SanzRequena Roberto,
PratsMontalbán José Manuel,
MartíBonmatí Luis,
AlberichBayarri Ángel,
GarcíaMartí Gracián,
Pérez Rosario,
Ferrer Alberto
Publication year - 2015
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.24805
Subject(s) - principal component analysis , population , nuclear medicine , dynamic contrast , correlation , medicine , pattern recognition (psychology) , artificial intelligence , statistics , mathematics , computer science , magnetic resonance imaging , radiology , geometry , environmental health
Background 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. Methods 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. 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). Conclusion The automatic PCA‐based approach minimizes the variability associated to obtaining individual volume‐based AIFs in DCE‐MR studies of the prostate. J. Magn. Reson. Imaging 2015;42:477–487.