FAST RECONSTRUCTION OF 3D PET DATA WITH ACCURATE STATISTICAL MODELING

This paper presents the results of combining high sensitivity 3D PET w hole-body acquisition followed by fast 2D iterative reconstruction met hods based on accurate statistical models. This combination is made po ssible by Fourier rebinning (FORE), which accurately converts a 3D dat a set to a set of 2D sinograms. The combination of volume imaging with statistical reconstruction allows improvement of noise-bias trade-off s when image quality is dominated by measurement statistics. The rebin ning of the acquired data into a 2D data set reduces the computation t ime of the reconstruction. For both penalized weighted least-squares ( PWLS) and ordered-subset EM: (OSEM) reconstruction methods, the useful ness of a realistic model of the expected measurement statistics is sh own when the data are pre-corrected for attenuation and random and sca ttered coincidences, as required for the FORE rebinning algorithm. The results presented are based on 3D simulations of whole-body scans tha t include the major statistical effects of PET acquisition and data co rrection procedures. As the PWLS method requires knowledge of the vari ance of the projection data, a simple model for the effect of FORE reb inning on data variance is developed.