Improving the image quality of IntraVascular Optical Coherence Tomography （IVOCT） through guidewire artifact removal can assist physicians in diagnosing cardiovascular diseases more accurately， which reduces the probabilities of misdiagnosis and missed diagnosis. Aiming at the difficulties of complex structure information and a large proportion of artifact areas in IVOCT images， a Structure-Enhanced Transformer Network （SETN） using Generative Adversarial Network （GAN） architecture was proposed for guidewire artifact removal of IVOCT images. Firstly， based on the ORiginal Image （ORI） backbone generation network for extracting texture features， the generator of GAN was combined with RTV （Relative Total Variation） image enhanced generation network in parallel to obtain image structure information. Next， during the artifact area reconstruction of ORI/RTV image， Transformer encoders focusing on the temporal/spatial domain information respectively were introduced to capture the contextual information and the correlation between texture/structure features of IVOCT image sequence. Finally， the structural feature fusion module was used to integrate the structural features of different levels into the decoding stage of the ORI backbone generation network， so that the generator was cooperated with the discriminator for completing the image reconstruction of the guidewire artifact area. Experimental results show that the guidewire artifact removal results of SETN are excellent in both texture and structure reconstruction. Besides， the improvement of IVOCT image quality after guidewire artifact removal is positive for both vulnerable plaque segmentation and lumen contour extraction tasks of IVOCT image.