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Uncertainty Maps in Dynamic Contrast-Enhanced MRI

ˆ S σ was found from the residuals of the fit, while the true covariances of the AIF, S0 and T10 were used. The proposed method was also tested on real data, acquired with the same imaging settings as in the simulation. The variances of S0 and T10 were found from 20 baseline images and the standard error from the calculation of T10. The covariance of the AIF was extracted from 10 measured AIFs. Results Figure 1 shows the results of the Monte Carlo simulation and the uncertainty estimation. The agree- ment was in general good but at high noise levels deviations are apparent especially for noise in the baseline signal (b) and in the baseline T10 value (c). Furthermore, the precision tended to deteriorate at high noise levels. Figure 2 (a) and (b) show a K trans map and a map of the standard deviation in K trans , respectively. In Figure 2 (c) and (d), the CVs of K trans , ve, and vp in the two pixels indicated in (a) are displayed. Discussion and conclusions The Monte Carlo simulations indicate that Eq. (2) accurately predicts the CV of the estimated parameters at moderate noise levels also for complicated sources of uncertainty such as the AIF. That Eq. (2) is limited to moderate noise was expected from the linear approximation. The example on real data shown in Figure 2 demonstrates that spatially resolved maps of uncertainty subdivided by origin are feasible in vivo.