A hybrid domain network for sparse view CT imaging based on Transformer HDTransformer was proposed to address the serious image noise artifacts caused by incomplete scanning data in computed tomography CT . Methods The main concept of the algorithm was to utilize a novel Transformer network to construct a processing flow suitable for multi-stage sparse view CT projection data and image data to improve the quality of sparse view CT image reconstruction. In comparison to existing two-stage hybrid domain processing methods this approach adopted a three-stage hybrid processing flow of image domain-projection domain-image domain enhancing imaging quality through the joint complementary information of multiple stages. Furthermore different Transformer blocks were designed based on the characteristics of noise and artifacts in data at different stages for differentiated processing. Moreover the algorithm adopted differentiable analytical reconstruction and projection operations to establish the conversion of data between projection domain and image domain ultimately achieving end-to-end high-quality sparse view CT imaging flow. Results Through Mayo data experimental verification the visual results showed that the processed CT images of different parts effectively suppressed noise artifacts. The quantization results showed that the peak signal-to-noise ratio and feature similarity of the processed CT images were better than those of the comparison method. Conclusion The qualitative and quantitative results of the experiment indicate that the proposed algorithm outperforms other algorithms in removing image artifacts and has higher quality verifying the effectiveness of this method.