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 method for analysis of functional activation images. This paper reports a quantitative evaluation of the sensitivity and accuracy of SPM, using a realistic simulator of PET image formation, which accounted for the main physical processes involved in PET, including attenuation, scatter, randoms, Poisson noise, and limited detector resolution. Activation foci of the brain were simulated by placing spheres of specified activities in particular locations. Using these data, the sensitivity and accuracy of SPM in detecting activation foci was measured for different versions of the SPM spatial normalization method and for an elastic warping method referred to as STAR (spatial transformation algorithm for registration). The STAR method resulted in relatively better registration and hence better detection of the activation foci. A secondary goal of the paper was to evaluate the improvement in detection sensitivity obtained by applying an atlas-based adaptive smoothing method instead of the usual Gaussian filtering method. The results indicate some limitations of statistical parametric mapping, assist in the correct interpretation of the SPM maps, and point to future research directions in functional image analysis.