  Positron emission tomography(PET) image reconstruction is an ill-posed
inverse problem and suffers from high level of noise due to limited counts
received. Recently deep neural networks especially convolutional neural
networks(CNN) have been successfully applied to PET image reconstruction.
However, the local characteristics of the convolution operator potentially
limit the image quality obtained by current CNN-based PET image reconstruction
methods. In this paper, we propose a residual swin-transformer based
regularizer(RSTR) to incorporate regularization into the iterative
reconstruction framework. Specifically, a convolution layer is firstly adopted
to extract shallow features, then the deep feature extraction is accomplished
by the swin-transformer layer. At last, both deep and shallow features are
fused with a residual operation and another convolution layer. Validations on
the realistic 3D brain simulated low-count data show that our proposed method
outperforms the state-of-the-art methods in both qualitative and quantitative
measures.
