Multicompartmental analysis of tracer clearance and its application to cerebral blood flow measurement

A new algorithm for computing cerebral blood flow is shown to overcome compartmental slippage and unstability problems associated with the conventional bicompartmental analysis. The tracer clearance curve is decomposed into a nonnegative linear combination of predetermined flow components. A weighted average of flows above (below) a fixed threshold yields the gray (white) matter flow. The accuracy and the stability of the new algorithm are analyzed by Monte-Carlo simulations, determining the effect of factors such as random error in tracer concentration, gray-white flow difference, tissue content of gray matter, and end-fit time. While the new algorithm requires 50-100% more CPU time and memory space than the bicompartmental method, its accuracy and stability is superior, especially as the conditions of the measurement deteriorate. The results suggest that more than twofold error reduction in measuring the blood flow in pathological brain tissue is possible.