The accurate mapping of the tumor blood volume (TBV) fraction (vb) is a highly desired imaging biometric goal. It is commonly thought that achieving this is difficult, if not impossible, when small molecule contrast reagents (CRs) are used for the T1-weighted (Dynamic-Contrast-Enhanced) DCE-MRI technique. This is because angiogenic malignant tumor vessels allow facile CR extravasation. Here, a three-site equilibrium water exchange model is applied to DCE-MRI data from the cerebrally-implanted rat brain U87 glioma, a tumor exhibiting rapid CR extravasation. Analyses of segments of the (and the entire) DCE data time-course with this "shutter-speed" pharmacokinetic model, which admits finite water exchange kinetics, allow TBV estimation from the first-pass segment. Pairwise parameter determinances were tested with grid searches of 2D parametric error surfaces. Tumor blood volume (vb), as well as ve (the extracellular, extravascular space volume fraction), and Ktrans (a CR extravasation rate measure) parametric maps are presented. The role of the Patlak Plot in DCE-MRI is also considered.

trans , the volume-weighted CR extravasation rate constant, and pb, the mole fraction (“population”) of tissue water in blood. The b -1 (unidirectional rate constant for water extravasation) value was fixed at 3.3 s -1 . With the thus-fitted K trans (0.28 min -1 ) and pb (0.038) as initial starting value and fixed, respectively, the blue curve shows the BALDERO fitting of the entire time-course using only K trans and po, the mole fraction of tissue water in extravascular extracellular space, as fitting parameters. The green curve, which largely overlaps the blue one, is a fitting of the data points occurring only after the first-pass. The negligible difference between these, and the relative continuity of the fitted segments, clearly indicates the dominance of different model parameters at different CR passage periods. (The “texture” of the fitted curves arises from that of the AIF, which is numerically incorporated into the analytical BALDERO.) To gain confidence in these fittings, parameter sensitivities to the DCE data were tested with comparisons that effected parametric grid searches. Figure 2 shows a contour plot of the natural logarithm of the chi square statistic, χ 2 = [Sdata(t) – Smodel(t)] 2