A Time-Adaptive Diffusion-Based CT Image Denoising Method by Processing Directional and Non-Local Information

Abstract

Low-dose computed tomography (CT) images are prone to noise and artifacts caused by photon starvation and electronic noise. Recently, researchers have explored the use of transformer-based neural networks combined with generative diffusion models, showing promising results in denoising CT images. Despite their high performance, these approaches often struggle to process crucial information in the input data, resulting in suboptimal image quality. To address this limitation, we propose Starformer, a novel transformer-based operation designed to extract non-local directional features essential for diagnostic accuracy while maintaining an acceptable computational complexity overhead. Starformer is seamlessly integrated into the time-adaptive schedules of a diffusion model, dynamically balancing global structural extraction and fine texture refinement throughout the diffusion process. This enables the generation of high-quality, realistic textures in the final denoised images. Extensive experimental results demonstrate the effectiveness of both approaches in enhancing CT image quality, with improvements of up to 15% in PSNR and 36% in SSIM, highlighting their superiority over state-of-the-art methods.