CycleGAN shows good performance with harmonization task. However, generative models have risk of structure modification. we proposed a cross-vendor harmonization model with paired CycleGAN based architecture for both high performance and structural consistency. We acquired 4 in-vivo dataset from Siemens and Philips scanner. For algorithm, we adapted CycleGAN for generation model, and we utilized L1 loss from pix2pix and patchGAN discriminator for structural consistency. Evaluations were performed both quantitatively and qualitatively. To quantitative evaluation, we assessed means of structural similarity index measure (SSIM). Proposed model shows better results compared to CycleGAN architecture.