Application progress of deep learning in chest low-dose computed tomography image denoising
10.3760/cma.j.cn121382-20250611-00044
- VernacularTitle:深度学习在胸部低剂量计算机断层扫描图像去噪中的应用进展
- Author:
Yunjian WU
1
;
Dapeng YAO
;
Ping GONG
;
Xiaofeng LI
Author Information
1. 徐州市肿瘤医院医学影像科，徐州 221000
- Keywords:
Deep learning;
Low-dose computed tomography;
Image denoising;
Supervised learning;
Unsupervised learning;
Self-supervised learning
- From:
International Journal of Biomedical Engineering
2025;48(5):501-506
- CountryChina
- Language:Chinese
-
Abstract:
Chest low-dose computed tomography (LDCT) is a widely utilized modality for lung cancer screening and follow-up in high-risk populations, owing to its low radiation dose. However, the diagnostic accuracy of LDCT is significantly constrained by inherent limitations, including elevated image noise and reduced contrast resolution. The potential for deep learning technologies to address these challenges through data-driven LDCT image denoising approaches has been demonstrated. In this review, the advantages and limitations of deep learning models were introduced, including supervised, unsupervised, and self-supervised learning. The potential and challenges of these models in clinical applications were analyzed, thereby providing a reference for subsequent research and clinical practice.
