Evaluation of Noise Properties in PSF-Based PET Image Reconstruction

The addition of accurate system modeling in PET image reconstruction results in images with distinct noise texture and characteristics. In particular, the incorporation of point spread functions (PSF) into the system model has been shown to visually reduce image noise, but the noise properties have not been thoroughly studied. This work offers a systematic evaluation of noise and signal properties in different combinations of reconstruction methods and parameters. We evaluate two fully-3D PET reconstruction algorithms: (1) OSEM with exact scanner line of response modeled (OSEM+LOR), (2) OSEM with line of response and a measured point spread function incorporated (OSEM+LOR+PSF), in combination with the effects of 4 post filtering parameters and 1-10 iterations. We used a modified NEMA IQ phantom, which was filled with 68Ge and consisted of 6 hot spheres of different sizes with a target/background ratio of 4:1. The phantom was scanned 50 times in 3D mode on a clinical system to provide independent noise realizations. Data were reconstructed with OSEM+LOR and OSEM+LOR+PSF using different reconstruction parameters. With access to multiple realizations, 4 metrics are adopted to quantify the noise characteristics in the reconstructed images. Image roughness and the standard deviation image are measures of the pixel-to-pixel variation, while NEMA and ensemble noises quantify the region-to-region variation. In addition to 4 noise metrics, we also evaluate signal to noise performance with accepted signal strength measures (recovery coefficient, SNR for quantitation), and study the relations between different metrics. From the analysis results, a linear correlation is observed between NEMA noise and ensemble noise for all different combinations of reconstruction methods and parameters, suggesting that NEMA style noise is a reasonable surrogate for ensemble noise when multiple realizations of scans are not available in practice. At the same number of iterations, the addition of PSF reduces image roughness for unfiltered images by roughly 35%, while the addition of PSF does not reduce NEMA style or ensemble noise. When noise is measured across realizations, the PSF based method offers slightly improved ( 7%) signal to noise performance across a range of reconstruction parameters.