  Computed Tomography (CT) is a medical imaging modality that can generate more
informative 3D images than 2D X-rays. However, this advantage comes at the
expense of more radiation exposure, higher costs, and longer acquisition time.
Hence, the reconstruction of 3D CT images using a limited number of 2D X-rays
has gained significant importance as an economical alternative. Nevertheless,
existing methods primarily prioritize minimizing pixel/voxel-level intensity
discrepancies, often neglecting the preservation of textural details in the
synthesized images. This oversight directly impacts the quality of the
reconstructed images and thus affects the clinical diagnosis. To address the
deficits, this paper presents a new self-driven generative adversarial network
model (SdCT-GAN), which is motivated to pay more attention to image details by
introducing a novel auto-encoder structure in the discriminator. In addition, a
Sobel Gradient Guider (SGG) idea is applied throughout the model, where the
edge information from the 2D X-ray image at the input can be integrated.
Moreover, LPIPS (Learned Perceptual Image Patch Similarity) evaluation metric
is adopted that can quantitatively evaluate the fine contours and textures of
reconstructed images better than the existing ones. Finally, the qualitative
and quantitative results of the empirical studies justify the power of the
proposed model compared to mainstream state-of-the-art baselines.
