CT理论与应用研究2025，Vol.34Issue(3)：419-426,8.DOI:10.15953/j.ctta.2025.097
一种深度学习强化的CT多相流测量重建算法
Deep-learning Enhanced CT Reconstruction Algorithm for Multiphase-flow Measurement
摘要
Abstract
Multiphase-flow measurement cannot effectively capture mesoscale dynamic structures owing to limitations of spatial and temporal resolutions of current measuring techniques.Dynamic X-ray computed tomography(CT),as a non-invasive multiphase-flow measurement technique,is promising for measuring the dynamic structures of multiphase flow.Focusing on the gas-liquid two-phase flow in multiphase flow,this paper addresses limited angle artifacts and excessive reconstruction time in mesoscale dynamic structures and proposes a U-Net-enhanced simultaneous iterative reconstruction technique(SIRT)reconstruction algorithm for bubble-structure measurements based on gas-liquid two-phase flow.Subsequently,based on the hardware design of a flowfield dynamic measurement system,which is a limited-angle dynamic X-ray CT system,a simulated gas-liquid two-phase flow dataset for training the deep-learning model is constructed from three-dimensional bubble structures obtained from hydrogel phantoms.The proposed method yields good results in the training and testing of the constructed dataset and significantly reduces the reconstruction time,thus providing a new technical approach for the high-spatiotemporal-resolution measurement of multiphase-flow mesoscale structures.关键词
深度学习/动态CT/多相流测量/有限角/重建算法Key words
deep learning/dynamic CT/multiphase flow measurement/limited-angle/reconstruction algorithm分类
信息技术与安全科学引用本文复制引用
陈浅,于宝地,秦艳玮,王孙洋,苏晓辉,金鑫,孟凡勇..一种深度学习强化的CT多相流测量重建算法[J].CT理论与应用研究,2025,34(3):419-426,8.基金项目
国家自然科学基金面上项目(基于深度学习的两相流实时成像方法(22178355)) （基于深度学习的两相流实时成像方法(22178355）
介科学与工程全国重点实验室自主部署课题(介科学思想视角下的机器学习模型泛化能力(MESO-24-A03)) （介科学思想视角下的机器学习模型泛化能力(MESO-24-A03）
中国科学院战略性先导科技专项(中国科学院化工冶金低碳变革技术及示范战略性先导科技专项(XDA0390501)). （中国科学院化工冶金低碳变革技术及示范战略性先导科技专项(XDA0390501）
