A linear back projection Pix2Pix (LBP-Pix2Pix) image reconstruction method, based on generative adversarial networks, is proposed to address the issues of heavy artifacts, high noise levels, and long processing times in optical tomography reconstruction. This method utilizes the LBP technique to reconstruct the absorption coefficient distribution within the object's cross-section. The initial reconstructed image and the true distribution are used as training samples for the Pix2Pix model. The optimal reconstruction model is obtained through adversarial training of the generator and discriminator. Using the model to process LBP-reconstructed images yields reconstructed images with fewer artifacts and clear edges. Five cross-sectional distributions are tested, and the results show that the reconstruction error range of the LBP-Pix2Pix method is 5.20%?13.15%, and the correlation coefficient range is 88.34%?99.08%. Compared with other reconstruction techniques, this method significantly enhances the imaging speed and image accuracy, presenting a novel image reconstruction approach for optical tomography.