A Self-Supervised Diffusion Model With Edge Prior for Unpaired LDCT Denoising.

Abstract

Low-dose computed tomography (LDCT) reduces health risks from radiation exposure but introduces imaging noise and artifacts. While numerous studies have employed deep learning for LDCT image denoising, the field continues to face significant challenges. Recent advancements have seen diffusion models applied to overcome issues of over-smoothness and unstable training inherent in prior deep learning approaches. However, the diffusion models face challenges in direct practical applications due to the extensive sampling steps, significant inference time required, and the need for hard-to-obtain paired data during training. To address these difficulties, this paper introduces a self-supervised diffusion model with edge prior for unpaired LDCT denoising. This method enables denoising within a lower-dimensional space, reducing computational complexity. Our proposed approach enhances denoised image clarity by applying prior edge constraints to compressed encodings; it employs a noise-conditioned encoding strategy to facilitate self-supervised image training, enabling the method to be applicable to unpaired CT data; and it utilizes compressed LDCT encoding as intermediate sampling results during the inference process, thereby accelerating sampling and reducing the time required for inference, making the method more real-time capable. Extensive validation across multiple datasets demonstrates that our method achieves competitive performance against state-of-the-art approaches in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and perceptual quality (LPIPS), while maintaining a practically acceptable inference time.