The invention discloses a particle filtering-based method of reconstructing static PET images, which comprises the steps of: (1) establishing a state space equation; (2) subjecting voxels to particle sampling; (3) computing a particle weight; (4) resampling the particles; (5) computing a particle concentration truth value and a particle weight truth value; and (6) computing a to-be-estimated concentration value of each voxel. By combining the particle filtering through a state space, the data statistic features and physiological property of PET are joined up to reconstruct a PET image, thereby the resolution and acutance of the image are improved, the true PET image is well restored, at the same time, a data model of noise in the PET is defined as Poisson distribution but not Gaussian distribution, which is more suitable for the actual condition of PET scanning, therefore, noises in the reconstruction process are more effectively filtered and optimized, and obtained reconstruction results are more approximate to the actual conditions of PET compared with that obtained by ML-EM (Maximum-Likelihood and Expectation-Maximization), FBP (Filtered Back Projection) and other conventional reconstruction methods, and the reconstruction effect is more excellent.