Assessment of SPM in perfusion brain SPECT studies. A numerical simulation study using bootstrap resampling methods.

Statistical parametric mapping (SPM) has become the technique of choice to statistically evaluate positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and single photon emission computed tomography (SPECT) functional brain studies. Nevertheless, only a few methodological studies have been carried out to assess the performance of SPM in SPECT. The aim of this paper was to study the performance of SPM in detecting changes in regional cerebral blood flow (rCBF) in hypo- and hyperperfused areas in brain SPECT studies. The paper seeks to determine the relationship between the group size and the rCBF changes, and the influence of the correction for degradations. The assessment was carried out using simulated brain SPECT studies. Projections were obtained with Monte Carlo techniques, and a fan-beam collimator was considered in the simulation process. Reconstruction was performed by using the ordered subsets expectation maximization (OSEM) algorithm with and without compensation for attenuation, scattering, and spatial variant collimator response. Significance probability maps were obtained with SPM2 by using a one-tailed two-sample t-test. A bootstrap resampling approach was used to determine the sample size for SPM to detect the between-group differences. Our findings show that the correction for degradations results in a diminution of the sample size, which is more significant for small regions and low-activation factors. Differences in sample size were found between hypo- and hyperperfusion. These differences were larger for small regions and low-activation factors, and when no corrections were included in the reconstruction algorithm.