Accelerating Diffusion: Task-Optimized latent diffusion models for rapid CT denoising.

Computed tomography (CT) systems are indispensable for diagnostics but pose risks due to radiation exposure. Low-dose CT (LDCT) mitigates these risks but introduces noise and artifacts that compromise diagnostic accuracy. While deep learning methods, such as convolutional neural networks (CNNs) and generative adversarial networks (GANs), have been applied to LDCT denoising, challenges persist, including difficulties in preserving fine details and risks of model collapse. Recently, the Denoising Diffusion Probabilistic Model (DDPM) has addressed the limitations of traditional methods and demonstrated exceptional performance across various tasks. Despite these advancements, its high computational cost during training and extended sampling time significantly hinder practical clinical applications. Additionally, DDPM's reliance on random Gaussian noise can reduce optimization efficiency and performance in task-specific applications. To overcome these challenges, this study proposes a novel LDCT denoising framework that integrates the Latent Diffusion Model (LDM) with the Cold Diffusion Process. LDM reduces computational costs by conducting the diffusion process in a low-dimensional latent space while preserving critical image features. The Cold Diffusion Process replaces Gaussian noise with a CT denoising task-specific degradation approach, enabling efficient denoising with fewer time steps. Experimental results demonstrate that the proposed method outperforms DDPM in key metrics, including PSNR, SSIM, and RMSE, while achieving up to 2 × faster training and 14 × faster sampling. These advancements highlight the proposed framework's potential as an effective and practical solution for real-world clinical applications.