Computed tomography (CT) is essential for diagnosing and managing various diseases, with contrast-enhanced CT (CECT) offering higher contrast images following contrast agent injection. Nevertheless, the usage of contrast agents may cause side effects. Therefore, achieving high-contrast CT images without the need for contrast agent injection is highly desirable. The main contributions of this paper are as follows: 1) We designed a GAN-guided CNN-Transformer aggregation network called GCTANet for the CECT image synthesis task. We propose a CNN-Transformer Selective Fusion Module (CTSFM) to fully exploit the interaction between local and global information for CECT image synthesis. 2) We propose a two-stage training strategy. We first train a non-contrast CT (NCCT) image synthesis model to deal with the misalignment between NCCT and CECT images. Then we trained GCTANet to predict real CECT images using synthetic NCCT images. 3) A multi-scale Patch hybrid attention block (MSPHAB) was proposed to obtain enhanced feature representations. MSPHAB consists of spatial self-attention and channel self-attention in parallel. We also propose a spatial channel information interaction module (SCIM) to fully fuse the two kinds of self-attention information to obtain a strong representation ability. We evaluated GCTANet on two private datasets and one public dataset. On the neck dataset, the PSNR and SSIM achieved were 35.46 +/- 2.783 dB and 0.970 +/- 0.020 , respectively; on the abdominal dataset, 25.75 +/- 5.153 dB and 0.827 +/- 0.073 , respectively; and on the MRI-CT dataset, 29.61 +/- 1.789 dB and 0.917 +/- 0.032 , respectively. In particular, in the area around the heart, where obvious movements and disturbances were unavoidable due to the heartbeat and breathing, GCTANet still successfully synthesized high-contrast coronary arteries, demonstrating its potential for assisting in coronary artery disease diagnosis. The results demonstrate that GCTANet outperforms existing methods.