Fast reconstruction of 3D PET data with accurate statistical modeling

This paper presents the results of combining high sensitivity 3D PET whole-body acquisition followed by fast 2D iterative reconstruction methods based on accurate statistical models. This combination is made possible by Fourier rebinning (FORE), which accurately converts a 3D data set to a set of 2D sinograms. The combination of volume imaging with statistical reconstruction allows improvement of noise-bias trade-offs when image quality is dominated by measurement statistics. The rebinning of the acquired data into a 2D data set reduces the computation time of the reconstruction. For both penalized weighted least-squares (PWLS) and ordered-subset EM: (OSEM) reconstruction methods, the usefulness of a realistic model of the expected measurement statistics is shown when the data are pre-corrected for attenuation and random and scattered coincidences, as required for the FORE rebinning algorithm. The results presented are based on 3D simulations of whole-body scans that include the major statistical effects of PET acquisition and data correction procedures. As the PWLS method requires knowledge of the variance of the projection data, a simple model for the effect of FORE rebinning on data variance is developed.