Jee Jongmin, Chang Won, Kim Euyoung, Lee Kyongjoon
Interdisciplinary Program of Bioengineering, Seoul National University, Seoul, South Korea.
Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, gyeonggi-Do, South Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, South Korea.
Comput Biol Med. 2025 Aug;194:110517. doi: 10.1016/j.compbiomed.2025.110517. Epub 2025 Jun 12.
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.
计算机断层扫描（CT）系统对诊断至关重要，但由于辐射暴露会带来风险。低剂量CT（LDCT）可降低这些风险，但会引入噪声和伪影，从而影响诊断准确性。虽然深度学习方法，如卷积神经网络（CNN）和生成对抗网络（GAN），已应用于LDCT去噪，但挑战依然存在，包括难以保留精细细节以及模型崩溃的风险。最近，去噪扩散概率模型（DDPM）解决了传统方法的局限性，并在各种任务中展现出卓越性能。尽管有这些进展，但其训练期间的高计算成本和延长的采样时间严重阻碍了实际临床应用。此外，DDPM对随机高斯噪声的依赖会降低任务特定应用中的优化效率和性能。为克服这些挑战，本研究提出了一种新颖的LDCT去噪框架，该框架将潜在扩散模型（LDM）与冷扩散过程相结合。LDM通过在低维潜在空间中进行扩散过程来降低计算成本，同时保留关键图像特征。冷扩散过程用特定于CT去噪任务的退化方法取代高斯噪声，从而能够以更少的时间步长进行高效去噪。实验结果表明，所提出的方法在关键指标上优于DDPM，包括峰值信噪比（PSNR）、结构相似性指数（SSIM）和均方根误差（RMSE），同时训练速度提高了2倍，采样速度提高了14倍。这些进展凸显了所提出框架作为实际临床应用的有效实用解决方案的潜力。
