测试技术学报2025，Vol.39Issue(4)：406-414,9.DOI:10.62756/csjs.1671-7449.2025051
一种基于多特征融合的低剂量CT去噪方法
A Low-Dose CT Denoising Method Based on Multi-Feature Fusion
摘要
Abstract
Deep learning has achieved a wide range of successful applications in the field of low-dose computed tomography(LDCT)denoising.To balance the relationship between LDCT image denoising and texture details during deep learning training,an LDCT denoising model based on multi-feature extraction and attention mechanism is proposed.The model consists of three branches.The first branch is the shallow edge feature extraction branch,so that the input LDCT image can be fully pre-trained.The second branch uses cross-convolution to explore more edge details in LDCT images.The third branch uses trainable Sobel and normal-dose CT edge labels to further enhance the performance of LDCT image edge details.Finally,the multi-feature fusion module is realized by using the attention mechanism.The experimental results on the American Association of Physicists in Medicine(AAPM)public dataset show that compared with the existing methods,the peak signal-to-noise ratio value of the CT image of the AAPM dataset processed by the new model is 33.637 6,and the structural similarity value is 0.916 9.This method can effectively remove noise and artifacts,while effectively retaining the structural information of the CT image.关键词
低剂量CT去噪/特征提取/边缘提取/注意力机制/交叉卷积/多特征融合Key words
low-dose CT denoising/feature extraction/edge extraction/attention mechanism/cross con-volution/multi-feature fusion分类
计算机与自动化引用本文复制引用
付学敬,桂志国,李志媛..一种基于多特征融合的低剂量CT去噪方法[J].测试技术学报,2025,39(4):406-414,9.基金项目
国家自然科学基金资助项目(61801438) （61801438）
国家重点研发计划资助项目(2023YFC3304900) （2023YFC3304900）
