Using γ photons to detect dynamic flow fields inside a cavity requires a fast image reconstruction algorithm. The traditional processing method is to collect all events first. Then, the algorithm processing is performed, such as OSEM. This study proposes an image reconstruction ( T-OSEM) algorithm that subdivides the response events according to the time stream. At the same time of continuous sampling data, the sampled data are divided into sub-sampling data sets according to the time period, and OSEM iteration is performed on the subset to achieve image reconstruction. The previous frame image is taken as the iterative input, and the correlation between frames is used to accelerate the convergence of iterative operation. The sampling of the data stream in T-OSEM is carried out simultaneously with the processing of the previous image frame. The image reconstruction process is accelerated by the multithreaded parallel operation. The relationship between the optimal number of subset events and the corresponding sampling time is studied to achieve the optimal reconstruction effect under the shortest sampling time. Experiments show that when the sampling time period reaches 1s, there is still a good particle tracking effect, and the mean structural similarity of the particle trajectory image is 0. 92. Results indicate that the T-OSEM algorithm is a good solution for dynamic image reconstruction.