The use of cerebral blood volume (CBV) maps generated from dynamic MRI studies tracking the bolus passage of paramagnetic contrast agents strongly depends on the signal-to-noise ratio (SNR) of the maps. The authors present a semianalytic model for the noise in CBV maps and introduce analytic and Monte Carlo techniques for determining the effect of experimental parameters and processing strategies upon CBV-SNR. CBV-SNR increases as more points are used to estimate the baseline signal level. For typical injections, maps made with 10 baseline points have 34% more noise than those made with 50 baseline points. For a given peak percentage signal drop, an optimum TE can be chosen that, in general, is less than the baseline T2. However, because CBV-SNR is relatively insensitive to TE around this optimum value, choosing TE approximately equal to T2 does not sacrifice much SNR for typical doses of contrast agent. The TR that maximizes spin-echo CBV-SNR satisfies TR/T1 approximately equal to 1.26, whereas as short a TR as possible should be used to maximize gradient-echo CBV-SNR. In general, CBV-SNR is maximized for a given dose of contrast agent by selecting as short an input bolus duration as possible. For image SNR exceeding 20-30, the gamma-fitting procedure adds little extra noise compared with simple numeric integration. However, for noisier input images, can be the case for high resolution echo-planar images, the covarying parameters of the gamma-variate fit broaden the distribution of the CBV estimate and thereby decrease CBV-SNR. The authors compared the analytic noise predicted by their model with that of actual patient data and found that the analytic model accounts for roughly 70% of the measured variability of CBV within white matter regions of interest.