Computed Tomography （CT） three-dimensional reconstruction technique improves the quality of three-dimensional model by upsampling volume data， and reduces the jagged edges， streak artifacts and discontinuous surface in the model， so as to improve the accuracy of disease diagnosis in clinical medicine. A CT three-dimensional reconstruction algorithm based on super-resolution network was proposed to solve the problem that the model after CT three-dimensional reconstruction remains unclear enough in the past. The network model is a Double Loss Refinement Network （DLRNET）， and the three-dimensional reconstruction of abdominal CT was performed by uniaxial super-resolution. The optimization learning module was introduced at the end of the network model， and besides the calculation of the loss between the baseline image and super-resolution image， the loss between the roughly reconstructed image in the network model and the baseline image was also calculated. In this way， with the force of optimization learning and double loss， the results closer to the baseline image were produced by the network. Then， spatial pyramid pooling and channel attention mechanism were introduced into the feature extraction module to learn the features of vascular tissues with different thickness degrees and scales. Finally， the upsampling method was used to dynamically generate the convolution kernel set， so that a single network model was able to complete the upsampling tasks with different scaling factors. Experimental results show that compared with Residual Channel Attention Network （RCAN）， the proposed network model improves the Peak Signal-to-Noise Ratio （PSNR） by 0.789 dB on average under 2， 3， and 4 scaling factors， showing that the network model effectively improves the quality of CT three-dimensional model， recovers the continuous detail features of vascular tissues to some extent， and has practicability.
