Linearization improves the repeatability of quantitative Dynamic Contrast-Enhanced MRI

We studied the effect of linearization on the repeatability of the Tofts and reference region models (RRM) for Dynamic Contrast-Enhanced MRI (DCE MRI). We compared the repeatabilities of these two linearized models, the standard non-linear version, and semi-quantitative methods of analysis. Simulated and experimental DCE MRI data from 12 rats with a flank tumor of C6 glioma acquired over three consecutive days were analyzed using four quantitative and semi-quantitative DCE MRI metrics. The quantitative methods used were: 1) Linear Tofts model (LTM), 2) Non-linear Tofts model (NTM), 3) Linear RRM (LRRM), and 4) Non-linear RRM (NRRM). The following semi-quantitative metrics were used: 1) Maximum enhancement ratio ( MER ), 2) time to peak ( TTP ), 3) initial area under the curve ( iauc64 ), and 4) slope . LTM and NTM were used to estimate K trans , while LRRM and NRRM were used to estimate K trans relative to muscle ( R Ktrans ). Repeatability was assessed by calculating the within-subject coefficient of variation ( wSCV ) and the percent intra-subject variation ( iSV ) determined with the Gage repeatability and reproducibility (R&R) analysis. The iSV for R Ktrans using LRRM was two-fold lower compared to NRRM at all simulated and experimental conditions. A similar trend was observed for the Tofts model, where LTM was at least 50% more repeatable than the NTM under all experimental and simulated conditions. The semi-quantitative metrics iauc64 and MER were as equally reproducible as K trans and R Ktrans estimated by LTM and LRRM respectively. The iSV for iauc64 and MER were significantly lower than the iSV for slope and TTP . In simulations and experimental results, linearization improves the repeatability of quantitative DCE MRI by at least 30%, making it as repeatable as semi-quantitative metrics.