Super-iterative image reconstruction in PET

摘要

Most current Positron Emission Tomography (PET) scanners use pixelated detector crystals, and the crystal pitch limits the sampling and the image resolution. In this paper we present a maximum-likelihood based method to go beyond the existing discrete sampling in PET scanners. After an initial standard image reconstruction, the projection of the reconstructed image is used to redistribute the counts of each original LOR among several subLORs. The new dataset with increased sampling is reconstructed again, obtaining improved image resolution without increasing the noise. The procedure can be repeated several times for further improvements, being each reconstruction a super-iteration. We validated the method with data acquired with the preclinical Super Argus PET/CT scanner. We used the NEMA NU4- 2008 for the Super Argus PET/CT scanner to quantitatively measure the image quality improvement, which resulted in a Recovery Coefficient (RC) increase of 14% for the smallest rod. Results with in-vivo acquisitions of a rat cardiac study injected with FDG also confirm the improvement in image quality. The proposed method can be considered a generalization of standard reconstruction algorithms, which is able to achieve better images at the expense of increasing the reconstruction time.