The image noise affected the results of quality estimation in PET image was investigated in this research. The best image quality can be used to find the adequate iteration numbers in PET image reconstruction process. Two phantoms, Huffman and Utah, were used in this research. The simulation was processed by using a Monte Carlo algorithm to simulate phantom in a Siemens ECAT PET scanner respectively. Flowering that an MLEM iterative algorithm named OSEM was used for reconstruction. The noise level on image was estimated by standard deviation calculation. The image quality evaluation used the SSIM and the PSNR two image quality indices. The noise in PET images is ascending with the iteration numbers increasing but the image quality contradicts to the numbers of iteration for a Huffman phantom. The image quality of filtered images, by using average filter, is significantly lower than the original images. This effect indicated that the noise affects the image quality calculations. Optimal quality of image appears later than the unfiltered image, which shows that the rising noise results in the optimal image detection. A Utah phantom was also used to examine this effect and got equal results. The noise in image estimated accurately the truly optimal iterative image can be finding easily.