Jae-Hun Kim1, Geun-Ho Im2,
Jehoon Yang1, Jung Hee Lee1
1Department of Radiology,
Samsung Medical Center, Seoul, Gang-Name, Korea, Republic of; 2Center
for Molecular & Cellular Imaging, Samsung Biomedical Research Institute,
Samsung Medical Center, Seoul, Korea, Republic of
Converging evidences have indicated that dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides parameters indicating permeability of tumor microvessels which has been shown to be closely related to angiogenesis. For quantification of DCE-MRI, determination of arterial input function (AIF) is required. However, a manual determination of AIF in MR images of a mouse is often difficult due to small spatial resolution or the location of the tumor. In this study, we propose an algorithm for automatic determination of AIF from mouse DCE-MRI data using clustering analysis (Kendalls coefficient of concordance). Our results showed that permeability parameters computed from our method were comparable with those from the manual determination of AIF in Ktrans (8.85 9.49 %), ve (4.65 2.34 %), and kep (11.72 6.70 %), except for vp (98.37 739.11 %). These findings show the feasibility of an automatic determination of AIF in the mouse data using KCC measurement.