Quantitative dynamic contrast-enhanced MRI for mouse models using automatic detection of the arterial input function

Abstract

Dynamic contrast-enhanced MRI (DCE-MRI) is widely accepted for the evaluation of cancer. DCE-MRI, a noninvasive measurement of microvessel permeability, blood volume and blood flow, is extremely useful for understanding disease mechanisms and monitoring therapeutic responses in preclinical research. For the accurate quantification of pharmacokinetic parameters using DCE-MRI, determination of the arterial input function (AIF) from a large arterial vessel near the tumor is required. However, a manual determination of AIF in mouse MR images is often difficult because of the small spatial dimensions or the location of the tumor. In this study, we propose an algorithm for the automatic detection of AIF from mouse DCE-MR images using Kendall's coefficient of concordance. The proposed method was tested with computer simulations and then applied to tumor-bearing mice (n = 8). Results from computer simulations showed that the proposed algorithm is capable of categorizing simulated AIF signals according to their noise levels. We found that the resulting pharmacokinetic parameters computed from our method were comparable with those from the manual determination of AIF, with acceptable differences in K(trans) (5.14 ± 3.60%), v(e) (6.02 ± 3.22%), v(p) (5.10 ± 7.05%) and k(ep) (5.38 ± 4.72%). The results of the current study suggest the usefulness of an automatically defined AIF using Kendall's coefficient of concordance for quantitative DCE-MRI in mouse models for cancer evaluation.