同位素2024，Vol.37Issue(4)：332-340,9.DOI:10.7538/tws.2024.youxian.003
基于投影误差优化网络的碳/碳材料CT稀疏角度重建方法
A Projection Error Optimization Neural Network Based Sparse CT Reconstruction Method for Carbon/Carbon Materials
金珂 1陈博 1金虎 1曾天辰 2周星明 1徐林 1孙跃文2
作者信息
- 1. 航天材料及工艺研究所,北京 100076
- 2. 清华大学核能与新能源技术研究院,北京 100091
- 折叠
摘要
Abstract
In 60Co based computed tomography(CT)of carbon components,reducing the number of sampling angles can significantly shorten detection time and improve detection efficiency.However,for conventional analytical reconstruction algorithms,sparse angle reconstruction images contain a large amount of noise and artifacts,which interfere with the detection of defects in the images and affect the quality evaluation of the inspected components by the detection system under fast detection conditions.This article proposes a sparse angle CT image reconstruction method based on neuralnetwork,which uses an untrained encoding decoding neural network to optimize the projection error of the reconstructed image,and uses the ADAM algorithm to optimize the total variation prior of the image.Compared with traditional deep learning reconstruction algorithms,this method does not require training sample sets and has stronger generalization ability and robustness.The results of simulation and practical experiments show that compared to traditional analytical and reconstruction algorithms,this method significantly improves the quality of reconstructed images,while retaining the detailed information of the detected components,and significantly suppresses artifacts and noise in the reconstructed images retaining image details and texture.This work can effectively improve image quality,eliminate the interference of artifacts on the identification of defects in graphics,and improve the ability to recognize defects in carbon components for the detection system.关键词
碳/碳复合材料/60Co CT检测/深度学习/稀疏角度重建Key words
carbon components/60Co CT detection/deep image prior/spar seview CT reconstruction分类
能源科技引用本文复制引用
金珂,陈博,金虎,曾天辰,周星明,徐林,孙跃文..基于投影误差优化网络的碳/碳材料CT稀疏角度重建方法[J].同位素,2024,37(4):332-340,9.
