Cormputed tomography (CT) scanning is an effective medical imaging modality widely used in clinical medicine for diagnosing various conditions. CT can generate three-dimensional images, thus providing more information than traditional two-dimensional radiographs. However, this comes at a cost, as it involves higher radiation, increased expense, and more time consumption. With the advancement of artificial intelligence, particularly the rise of computer vision, researchers have explored using deep convolutional neural networks and generative adversarial networks for low-radiation CT reconstruction tasks. Yet, existing CT reconstruction methods from X-ray images often focus on pixel-level difference metrics, neglecting the perceptual differences considered by the human visual system, especially in accurately restoring internal details in medical images. In response to this, this paper introduces the auto-encoder-based generative adversarial network (AECT-GAN) model, which integrates an auto-encoder structure and Sobel Gradient Guidance (SGG) mechanism within the discriminator, aiming to enhance the fidelity of image detail reproduction. Experimental validation on the LIDC-IDRI lung CT dataset has demonstrated our AECT-GAN method’s superiority in qualitative and quantitative evaluations, notably achieving significant improvements in preserving fine contours and textures in reconstructed images. Furthermore, applying this model to the IXI brain MRI dataset conclusively proves its widespread applicability and outstanding performance in the medical imaging domain.