Detection of compartmental slippage in noninvasive rCBF measurements

Noninvasive measurements of regional cerebral blood flow (rCBF), using the Xe-133 clearance technique and a two-compartment open model for data analysis, may produce false numerical results when distinction between compartments is poor. For rapid detection of error conditions of that kind, we propose a three-dimensional graphic display of the quality of fit to the original clearance curve, based on bivariate simulations of clearance constants. This procedure may follow rCBF computation, irrespective of the main algorithm used. The discriminating power of this method is demonstrated in two characteristic routine rCBF measurements by gradual addition of random noise to the original data.