NIMG-04ADAPTIVE AND STANDARD PHARMACOKINETIC MODEL SELECTION TECHNIQUES IN QUANTITATIVE ANALYSIS OF DYNAMIC CONTRAST ENHANCED MRI STUDIES

Abstract

In quantitative analysis of tissue vascular permeability using Dynamic-Contrast-Enhanced (DCE) MR data analysis, one of the main challenges is to choose the best Pharmacokinetic (PK) model among competing models to describe the behavior of the time trace of contrast agent (CA) concentration. The F-Statistic (F-test), Akaike-Information-Criterion (AIC), Bayesian-Information-Criterion (BIC), and Log-Likelihood-Ratio (LLR) tests are the most widely used classical Model-Selection (MS) techniques. Our group has recently introduced an Adaptive-MS (AMS) method to perform PK-MS in DCE-MRI data. This study investigates the performance of different MS techniques (Classical and Adaptive) for selection of the PK model as well as the impact of the recruited technique on the estimation of PK parameters. In this study, three physiologically Nested Models were used to describe possible physiological conditions of underlying tissue pathology as Model 1, 2, and 3 respectively: The tissue-vascular-compartment is filled with CA with no outward-leakage, with outward-leakage but no evidence of back-flux, and with both outward-and backward-flux. Plasma volume vp, vp, and ktrans, vp, ktrans and kep, are the PK parameters for Models 1, 2 and 3 respectively. 21 Arterial Input Function curves were used to simulate the CA profile. By varying PK parameters (time-resolution = 5.03 sec), 9072 time traces for 12 levels of SNRs were generated. Then confusion matrices were computed for all the MS techniques over 9072 tested hypotheses. The performance of the AMS is superior to the other classical techniques. Other techniques show performances in the following order: LLR, BIC, AICc, AIC and F-Test. All techniques never miss any leaky tissue. Also the AMS generates significantly less-biased estimates of PK parameters compared to the classical techniques while the LLR and BIC methods outperform the other classical techniques. This study suggests that AMS, LLR, and BIC are the best candidates among the MS techniques for PK analysis of DCE-MRI data.