Research of Denoising Methods for Low-Dose CT Images

低剂量CT技术在显著降低患者辐射剂量的同时，不可避免地引入多样化的噪声与伪影，其强度与分布特性因成像条件而异，对图像质量及临床诊断准确性构成严峻挑战。传统图像去噪方法通常基于先验知识构建数学模型，虽能有效抑制部分噪声，但其优化过程依赖人工参数调谐，存在计算复杂度高、图像细节保留不足等固有缺陷。近年来，基于深度学习的去噪方法凭借其强大的非线性特征提取与端到端优化能力，在处理复杂噪声场景时展现出显著优势。本文系统性地介绍了低剂量CT图像去噪领域的研究进展：首先剖析了传统方法的理论框架及其局限性；随后重点探讨深度学习方法的技术原理、代表性模型架构及其在医学影像中的创新应用；最后，总结当前技术面临的核心挑战，并展望未来研究方向，旨在为低剂量CT成像技术的优化与临床转化提供理论依据与技术参考。While significantly reducing patient radiation exposure, low-dose CT technology inevitably introduces diverse noise and artifacts, whose intensity and distribution characteristics vary with imaging conditions, posing a serious challenge to image quality and clinical diagnostic accuracy. Traditional image denoising methods, typically based on prior knowledge to construct mathematical models, can effectively suppress some noise. However, their optimization process relies on manual parameter tuning, exhibiting inherent limitations such as high computational complexity and insufficient preservation of image details. In recent years, deep learning-based denoising methods have demonstrated significant advantages in handling complex noise scenarios, leveraging their powerful nonlinear feature extraction and end-to-end optimization capabilities. This paper systematically introduces the research progress in the field of low-dose CT image denoising: first, it analyzes the theoretical frameworks and limitations of traditional methods; then, it focuses on the technical principles of deep learning methods, representative model architectures, and their innovative applications in medical imaging; finally, it summarizes the core challenges currently faced by the technology and outlines future research directions, aiming to provide theoretical foundations and technical references for the optimization and clinical translation of low-dose CT imaging technology.