A STUDY ON STATISTICALLY RELIABLE AND COMPUTATIONALLY EFFICIENT ALGORITHMS FOR GENERATING LOCAL CEREBRAL BLOOD-FLOW PARAMETRIC IMAGES WITH POSITRON EMISSION TOMOGRAPHY

With the advent of positron emission tomography (PET), a variety of techniques have been developed to measure local cerebral blood flow (LCBF) noninvasively in humans. It is essential that the techniques developed should be statistically reliable and computationally efficient. A potential class of techniques, which includes linear least squares (LS), linear weighted least squares (WLS), linear generalized least squares (GLS), and linear generalized weighted least squares (GWLS), is proposed. The statistical characteristics of the new methods were examined by computer simulation. We present a comparison of these four methods with two other rapid estimation techniques developed by Huang et al. [1] and Alpert [2], and two classical methods, the unweighted and weighted nonlinear least squares regression which are supposed to have optimal statistical properties. The results show that the new methods can take full advantage of the contribution from the fine temporal sampling data of modern tomographs, and thus provide statistically reliable estimates that are comparable to those obtained from nonlinear least squares regression. The new methods also have high computational efficiency, and the parameters can be estimated directly from operational equations in one single step. Therefore, they can potentially be used in image-wide estimation of local cerebral blood How and distribution volume with positron emission tomography.