Accuracy and sensitivity of detection of activation foci in the brain via statistical parametric mapping: A study using a PET simulator

Statistical parametric mapping (SPM) is currently the most widely used meth od for analysis of functional activation images. This paper reports a quant itative evaluation of the sensitivity and accuracy of SPM, using a realisti c simulator of PET image formation, which accounted for the main physical p rocesses involved in PET, including attenuation, scatter, randoms, Poisson noise, and limited detector resolution. Activation foci of the brain were s imulated by placing spheres of specified activities in particular locations . Using these data, the sensitivity and accuracy of SPM in detecting activa tion foci was measured for different versions of the SPM spatial normalizat ion method and for an elastic warping method referred to as STAR (spatial t ransformation algorithm for registration). The STAR method resulted in rela tively better registration and hence better detection of the activation foc i. A secondary goal of the paper was to evaluate the improvement in detecti on sensitivity obtained by applying an atlas-based adaptive smoothing metho d instead of the usual Gaussian filtering method. The results indicate some limitations of statistical parametric mapping, assist in the correct inter pretation of the SPM maps, and point to future research directions in funct ional image analysis. (C) 2001 Academic Press.