Statistical modeling of positron emission tomography images in wavelet space

A new method is introduced for the analysis of multiple studies measured wi th emission tomography. Traditional models of statistical analysis (ANOVA, ANCOVA and other linear models) are applied not directly on images but on t heir correspondent wavelet transforms. Maps of model effects estimated from these models are filtered using a thresholding procedure based on a simple Bonferroni correction and then reconstructed. This procedure inherently re presents a complete modeling approach and therefore obtains estimates of th e effects of interest (condition effect, difference between conditions, cov ariate of interest, and so on) under the specified statistical risk. By per forming the statistical modeling step in wavelet space, the procedure allow s the direct estimation of the error for each wavelet coefficient: hence, t he local noise characteristics are accounted for in the subsequent filterin g. The method was validated by use of a null dataset and then applied to ty pical examples of neuroimaging studies to highlight conceptual and practica l differences from existing statistical parametric mapping approaches.