针对动态正电子发射成像（PET）Bayesian重建，提出一种区域时空先验(RST)模型。RST先验充分利用动态PET序列图像的空间和时间信息，在2维空间和1维时间上对噪声进行双重约束。为验证所提方法的有效性，进行人体脑部18F-FDG动态PET计算机仿真实验。实验结果表明，较其他经典重建方法，通过RST先验的引入，所提方法对于动态PET图像重建质量有着很好的改善，同时提高了病灶区域18F-FDG流入速率Ki估计准确度。

For Bayesian dynamic Positron emission tomography (PET) reconstruction, a regional spatial-temporal prior (RST) model is proposed in this paper. The RST exploits sufficiently both the spatial and temporal information, and could suppress the noise in both two spatial dimensions and one temporal dimension. Computer simulations of brain 18F-FDG dynamic PET was conducted to validate the proposed approach. The comparison with other classical reconstruction methods shows that the proposed approach performs better in dynamic PET reconstruction and the results are more accurately estimating of the influx rate Ki of 18F-FDG in the lesion region.