Characterization and simulation of noise in PET images reconstructed with OSEM: Development of a method for the generation of synthetic images

Abstract

IntroductionThe goals of the study are to characterize imaging properties in 2D PET images reconstructed with the iterative algorithm Ordered-Subset Expectation Maximization (OSEM) and to propose a new method for the generation of synthetic images. Material and methodsThe noise is analyzed in terms of its magnitude, spatial correlation, and spectral distribution through standard deviation, Autocorrelation Function, and Noise Power Spectrum (NPS), respectively. Their variations with position and activity level are also analyzed. This noise analysis is based on phantom images acquired from 18F uniform distributions. Experimental Recovery Coefficients of hot spheres in different backgrounds are employed to study the spatial resolution of the system through Point Spread Function (PSF). The NPS and PSF functions provide the baseline for the proposed simulation method: convolution with PSF as kernel and noise addition from NPS. ResultsThe noise spectral analysis shows that the main contribution is of random nature. It is also proven that attenuation correction does not alter noise texture but it modifies its magnitude. Finally, synthetic images of two phantoms, one of them an anatomical brain, are quantitatively compared with experimental images showing a good agreement in terms of pixel values and pixel correlations. Thus, the Contrast to Noise Ratio for the biggest sphere in the NEMA IEC phantom is 10.7 for the synthetic image and 8.8 for the experimental image. ConclusionsThe properties of the analyzed OSEM-PET images can be described by NPS and PSF functions. Synthetic images, even anatomical ones, are successfully generated by the proposed method based on the NPS and PSF.