{"title":"基于光谱计算机断层扫描深度先验的多材料重建方法","authors":"Xiao-Kun Yu, Ailong Cai, Lei Li, Bin Yan","doi":"10.1145/3548636.3548642","DOIUrl":null,"url":null,"abstract":"Spectral computed tomography (Spectral CT) has attracted more and more attention because of its ability of material discrimination. However, as the number of materials increases, it becomes more difficult to decompose the material according to the polychromatic projection. This paper presents a direct multi-material reconstruction method, in which a deep convolutional neural network (CNN)-based prior is incorporated into the optimization model. The efficient iterative algorithm is designed under the framework of the alternating direction method of multipliers (ADMM). The numerical experiments further validate the superiority of the proposed method in multi-material reconstruction and noise suppression.","PeriodicalId":384376,"journal":{"name":"Proceedings of the 4th International Conference on Information Technology and Computer Communications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multi-material Reconstruction Method Based On Deep Prior of Spectral Computed Tomography\",\"authors\":\"Xiao-Kun Yu, Ailong Cai, Lei Li, Bin Yan\",\"doi\":\"10.1145/3548636.3548642\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Spectral computed tomography (Spectral CT) has attracted more and more attention because of its ability of material discrimination. However, as the number of materials increases, it becomes more difficult to decompose the material according to the polychromatic projection. This paper presents a direct multi-material reconstruction method, in which a deep convolutional neural network (CNN)-based prior is incorporated into the optimization model. The efficient iterative algorithm is designed under the framework of the alternating direction method of multipliers (ADMM). The numerical experiments further validate the superiority of the proposed method in multi-material reconstruction and noise suppression.\",\"PeriodicalId\":384376,\"journal\":{\"name\":\"Proceedings of the 4th International Conference on Information Technology and Computer Communications\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 4th International Conference on Information Technology and Computer Communications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3548636.3548642\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 4th International Conference on Information Technology and Computer Communications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3548636.3548642","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi-material Reconstruction Method Based On Deep Prior of Spectral Computed Tomography
Spectral computed tomography (Spectral CT) has attracted more and more attention because of its ability of material discrimination. However, as the number of materials increases, it becomes more difficult to decompose the material according to the polychromatic projection. This paper presents a direct multi-material reconstruction method, in which a deep convolutional neural network (CNN)-based prior is incorporated into the optimization model. The efficient iterative algorithm is designed under the framework of the alternating direction method of multipliers (ADMM). The numerical experiments further validate the superiority of the proposed method in multi-material reconstruction and noise suppression.