Iterative versus Filtered Backprojection Reconstruction for Statistical Parametric Mapping of PET Activation Measurements: A Comparative Case Study

The significance of task-induced cerebral blood flow responses, assessed using statistical parametric mapping, depends, among other things, on the signal-to-noise ratio (SNR) of these responses. Generally, positron emission tomography sinograms of H(2)(15)O activation studies are reconstructed using filtered backprojection (FBP). Alternatively, the acquired data can be reconstructed using an iterative reconstruction procedure. It has been demonstrated that the application of iterative reconstruction methods improves image SNR as compared with FBP. The aim of this study was to compare FBP with iterative reconstruction, to assess the statistical power of H(2)(15)O-PET activation studies using statistical parametric mapping. For this case study, PET data originating from a bimanual motor task were reconstructed using both FBP and maximum likelihood expectation maximization (ML-EM), an iterative algorithm. Both resulting data sets were statistically analyzed using statistical parametric mapping. It was found, with this dataset, that the statistical analysis of the iteratively reconstructed data confirm the a priori expected physiological response. In addition, increased Z scores were obtained in the iteratively reconstructed data. In particular, for the expected task-related response, activation of the posterior border of the left angular gyrus, the Z score increased from 3.00 to 3.96. Furthermore, the number of statistically significant clusters doubled while their volume increased by more than 50%. In conclusion, iterative reconstruction has the potential to increase the statistical power in H(2)(15)O-PET activation studies as compared with FBP reconstruction.