Assessment of iterative image reconstruction techniques for small-animal PET imaging applications

摘要

The purpose of this study is to assess the performance of iterative reconstruction methods, using phantom data from a prototype small-animal PET system. The algorithms compared are the simultaneous versions of ART (SART), EM-ML, ISRA WLS and a new iterative algorithm we have introduced under the short name ISWLS. The evaluation study was based on reconstructed image quality, as it is derived from visual inspection, cross-correlation coefficient and CNRs (contrast-to-noise ratios) of specific ROIs (region-of-interest). In general EM-ML and ISRA present similar reconstruction time and minor differences in reconstructed image quality. Slightly superior performances show WLS and SART while ISWLS improves reconstruction resolution at the edges of the field of view.