SIGNAL-TO-NOISE ANALYSIS OF CEREBRAL BLOOD-VOLUME MAPS FROM DYNAMIC NMR IMAGING STUDIES

The use of cerebral blood volume (CBV) maps generated from dynamic MRI studies tracking the bolus passage of paramagnetic contrast agents st rongly depends on the signal-to-noise ratio (SNR) of the maps, The aut hors present a semianalytic model for the noise in CBV maps and introd uce analytic and Monte Carlo techniques for determining the effect of experimental parameters and processing strategies upon CBV-SNR, CBV-SN R increases as more points are used to estimate the baseline signal le vel, 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 approximate t o T2 does not sacrifice much SNR for typical doses of contrast agent. The TR that maximizes spin-echo CBV-SNR satisfies TR/T1 approximate to 1.26, whereas as short a TR as possible should be used to maximize gr adient-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 po ssible, For image SNR exceeding 20-30, the Gamma-fitting procedure add s little extra noise compared with simple numeric integration, However , for noisier input images, as can be the case for high resolution ech o-planar images, the covarying parameters of the Gamma-variate fit bro aden the distribution of the CBV estimate and thereby decrease CBV-SNR , The authors compared the analytic noise predicted by their model wit h that of actual patient data and found that the analytic model accoun ts for roughly 70% of the measured variability of CBV within white mat ter regions of interest.