重庆工商大学学报（自然科学版）2024，Vol.41Issue(5)：38-48,11.DOI:10.16055/j.issn.1672-058X.2024.0005.005
基于Transformer块的混合域网络稀疏角度CT成像
Hybrid Domain Network for Sparse View CT Imaging Based on Transformer Blocks
张庭宇 1吴凡 1金潼 1孙宇 1刘进 1亢艳芹1
作者信息
- 1. 安徽工程大学计算机与信息学院,安徽芜湖 241000
- 折叠
摘要
Abstract
Objective A hybrid domain network for sparse view CT imaging based on Transformer(HDTransformer)was proposed to address the serious image noise artifacts caused by incomplete scanning data in computed tomography(CT).Methods The main concept of the algorithm was to utilize a novel Transformer network to construct a processing flow suitable for multi-stage sparse view CT projection data and image data to improve the quality of sparse view CT image reconstruction.In comparison to existing two-stage hybrid domain processing methods,this approach adopted a three-stage hybrid processing flow of image domain-projection domain-image domain,enhancing imaging quality through the joint complementary information of multiple stages.Furthermore,different Transformer blocks were designed based on the characteristics of noise and artifacts in data at different stages for differentiated processing.Moreover,the algorithm adopted differentiable analytical reconstruction and projection operations to establish the conversion of data between projection domain and image domain,ultimately achieving end-to-end high-quality sparse view CT imaging flow.Results Through Mayo data experimental verification,the visual results showed that the processed CT images of different parts effectively suppressed noise artifacts.The quantization results showed that the peak signal-to-noise ratio and feature similarity of the processed CT images were better than those of the comparison method.Conclusion The qualitative and quantitative results of the experiment indicate that the proposed algorithm outperforms other algorithms in removing image artifacts and has higher quality,verifying the effectiveness of this method.关键词
深度学习/图像修复/Transformer模块/混合域Key words
deep learning/image restoration/Transformer blocks/hybrid domain分类
数理科学引用本文复制引用
张庭宇,吴凡,金潼,孙宇,刘进,亢艳芹..基于Transformer块的混合域网络稀疏角度CT成像[J].重庆工商大学学报（自然科学版）,2024,41(5):38-48,11.
