Low-dose CT denoising method with CNN and Transformer to preserve tiny details
10.3969/j.issn.1005-202X.2024.07.009
- VernacularTitle:一种用于低剂量CT的微小细节保护CNN与Transformer融合去噪方法
- Author:
Xiaozeng LI
1
;
Baozhu WANG
;
Zhitao GUO
;
Jui Sharmin SHANAZ
Author Information
1. 河北工业大学电子信息工程学院,天津 300401
- Keywords:
low-dose computed tomography;
image denoising;
deep learning;
tiny detail preservation
- From:
Chinese Journal of Medical Physics
2024;41(7):842-850
- CountryChina
- Language:Chinese
-
Abstract:
Given that low-dose computed tomography significantly amplifies image noise due to the mitigation of radiation exposure,which degrades image quality and lowers the precision of clinical diagnoses,a novel model incorporating convolutional neural network and Transformer is established,in which an intra-patch feature extraction module is used to effectively preserve tiny details in the image.A double attention Transformer is constructed by incorporating a multiple-input channel attention module into the self-attention for tackling the problem of incorrect restoration of texture details during denoising using Swin Transformer.AAPM dataset is used for testing,and the results demonstrate that the proposed algorithm not only surpasses the existing algorithms in denoising performance,but also excels in preserving tiny details in the image.
