Quantifying lumbar vertebral perfusion by a Tofts model on DCE-MRI using segmental versus aortic arterial input function

The purpose of this study was to investigate the influence of arterial input function (AIF) selection on the quantification of vertebral perfusion using axial dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). In this study, axial DCE-MRI was performed on 2 vertebrae in each of eight healthy volunteers (mean age, 36.9 years; 5 men) using a 1.5-T scanner. The pharmacokinetic parameters K-trans, v(e), and v(p), derived using a Tofts model on axial DCE-MRI of the lumbar vertebrae, were evaluated using various AIFs: the population-based aortic AIF (AIF_PA), a patient-specific aortic AIF (AIF_A) and a patient-specific segmental arterial AIF (AIF_SA). Additionally, peaks and delay times were changed to simulate the effects of various AIFs on the calculation of perfusion parameters. Nonparametric analyses including the Wilcoxon signed rank test and the Kruskal-Wallis test with a Dunn-Bonferroni post hoc analysis were performed. In simulation, K-trans and v(e) increased as the peak in the AIF decreased, but v(p) increased when delay time in the AIF increased. In humans, the estimated K-trans and v(e) were significantly smaller using AIF_A compared to AIF_SA no matter the computation style (pixel-wise or region-of-interest based). Both these perfusion parameters were significantly greater using AIF_SA compared to AIF_A.

Automated summary

This study investigated the impact of AIF selection on vertebral perfusion quantification using DCE-MRI. Simulation results showed that decreasing peak in AIF increased K-trans and v(e), while increasing delay time in AIF increased v(p). In human subjects, K-trans and v(e) were significantly smaller with AIF_A compared to AIF_SA, while both parameters were significantly greater with AIF_SA compared to AIF_A.