Generative Adversarial framework for CT image denoising using Self-Attentive Residual UNet and Patch GAN Discriminator

Computed Tomography (CT) is an indispensable tool to identify various health conditions. In Low Dose (LDCT) imaging, lower radiation is frequently chosen to minimize the impact of radiation on the human body, but it results in degraded image quality and subsequently generates noise in CT images. CT images are regularly influenced by Gaussian noise. The noise in CT images obscures fine anatomical details, thereby impacting accurate diagnostic precision. This is a challenging task for traditional image denoising approaches to maintain trade-off between reducing noise and preserving image information. Hence in LDCT images, lowering the radiation while enhancing the image quality is a critical challenge in clinical images. The main study explored the application of a GAN-based model that included the use of a self-attentive UNet generator to extract both local and global contextual data with a Patch GAN discriminator. The discriminator is evaluated to find the realism of local patches to maintain fine structural details. The quantitative measures such as the average Peak Signal to Noise Ratio (PSNR) value of 35.15 dB, and Structural Similarity Index Measure (SSIM) value of 0.92 at noise variance (s = 10) to examine the effectiveness of the model with respect to visual quality and clarity. The GAN-based model shows superior performance than ResNet50, UNet, and DnCNN models, showing that the GAN-based model is an optimistic denoising technique in LDCT images to suppress noise in LDCT images.