Super-resolution of Pneumocystis carinii pneumonia CT via self-attention GAN

Computed tomography (CT) examination plays an important role in screening suspected and confirmed patients in pneumocystis carinii pneumonia (PCP), and the efficient acquisition of high-quality medical CT images is essential for the clinical application of computer-aided diagnosis technology. Therefore, improving the resolution of CT images of pneumonia is a very important task.

Aiming at the problem of how to recover the texture details of the reconstructed PCP CT super-resolution image, we propose the image super-resolution reconstruction model based on self-attention generation adversarial network (SAGAN). In the SAGAN algorithm, a generator based on self-attention mechanism and residual module is used to transform a low-resolution image into a super-resolution image. A discriminator based on depth convolution network tries to distinguish the difference between the reconstructed super-resolution image and the real super-resolution image. In terms of loss function construction, on the one hand, the Charbonnier content loss function is used to improve the accuracy of image reconstruction, and on the other hand, the feature value before activation of the pre-trained VGGNet is used to calculate the perceptual loss to achieve accurate texture detail reconstruction of super-resolution images.

Experimental results show that our SAGAN algorithm is superior to other state-of-the-art algorithms in both peak signal-to-noise ratio (PSNR) and structural similarity score (SSIM). Specifically, our SAGAN method can obtain 31.94 dB which is 1.53 dB better than SRGAN on Set5 dataset for 4 enlargements.

Our SAGAN method can reconstruct more realistic PCP CT images with clear texture, which can help experts diagnose the condition of PCP.