Enhancing CT image resolution with a hybrid transformer-based generative adversarial network

Computed tomography (CT) imaging systems often produce images with lower resolution than ideal, particularly when using low-dose protocols to minimize radiation exposure. To address this limitation, we propose CTMTGAN, a novel super-resolution analysis method for enhancing CT image resolution using Generative Adversarial Networks (GANs). CTMTGAN integrates VMamba's TDSS module with Transformer-based attention blocks in its generator, trained with a PatchGAN discriminator and VGG-19 perceptual feature extractor. Experimental results demonstrate that CTMTGAN outperforms state-of-the-art methods in no-reference image quality metrics, producing clearer details, sharper edges, and fewer artifacts. Its ability to generate high-quality, high-resolution CT images suggests potential to improve diagnostic accuracy in clinical settings.