Fast reconstruction of 3-D PET data with accurate statistical modeling

This paper presents the results of combining a 3-D PET whole-body acquisition followed by Fourier rebinning (FORE), with a fast 2-D iterative reconstruction method based on an accurate statistical model such as the penalized weighted least-squares (PWLS) functional. 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 2-D data set reduces the computation time of the reconstruction. For both PWLS and the ordered-subset EM (OSEM) reconstruction method, the usefulness of a realistic model for the expected measurement statistics is shown when the data is pre-corrected for attenuation and random and scattered coincidences, as required for the FORE rebinning algorithm. The results presented are based on simulations of whole-body scan that include the major statistical effects of PET data acquisition and correction procedures. As the PWLS algorithm requires the knowledge of the variance of the projection data, a simple and realistic model for the effect of FORE rebinning on variance has been developed