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

作　　者：金珂[1] 陈博 金虎[1] 曾天辰 周星明[1] 徐林[1] 孙跃文[2] JIN Ke;CHEN Bo;JIN Hu;ZENG Tianchen;ZHOU Xingming;XU Lin;SUN Yuewen(Aerospace Research Institute of Materials&Processing Technology,Beijing 100076,China;Institute of Nuclear and New Energy Technology,Tsinghua University,Beijing 100091,China)

机构地区：[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

出　　处：《同位素》2024年第4期332-340,共9页Journal of Isotopes

摘　　要：在采用^(60)Co作为射线源的碳/碳复合材料的计算机断层扫描(CT)中,降低采样角度数量可以显著缩短检测时间,提升检测效率。然而常规的解析重建算法,稀疏角度的重建图像中包含大量的噪声和伪影,干扰图像中缺陷的检出,影响检测系统在快速检测条件下对被检构件的质量评价。本研究提出了一种基于投影误差优化神经网络的稀疏角度CT图像重建方法,采用未训练的编码-解码卷积神经网络优化重建图像的投影误差,结合图像的总变分先验,采用自适应动量估计(ADAM)算法进行优化。与传统的深度学习重建算法相比,该方法无需训练样本集,具备更强的泛化能力和鲁棒性。CT检测实验结果表明,该方法相比于传统的解析和重建算法,重建图像质量大幅提升,在保留被检测构件细节信息的同时,显著抑制了重建图像中的伪影与噪声。In ^(60)Co 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 ^(60)Co CT检测 深度学习 稀疏角度重建 

分 类 号：TL99[核科学技术—核技术及应用]