{"title":"Uconnect:利用 U 型网络连接能量盒进行协同频谱 CT 重构","authors":"Zhihan Wang;Alexandre Bousse;Franck Vermet;Jacques Froment;Béatrice Vedel;Alessandro Perelli;Jean-Pierre Tasu;Dimitris Visvikis","doi":"10.1109/TRPMS.2023.3330045","DOIUrl":null,"url":null,"abstract":"Spectral computed tomography (CT) offers the possibility to reconstruct attenuation images at different energy levels, which can be then used for material decomposition. However, traditional methods reconstruct each energy bin individually and are vulnerable to noise. In this article, we propose a novel synergistic method for spectral CT reconstruction, namely, Uconnect. It utilizes trained convolutional neural networks (CNNs) to connect the energy bins to a latent image so that the full binned data is used synergistically. We experiment on two types of low-dose data: 1) simulated and 2) real patient data. Qualitative and quantitative analysis show that our proposed Uconnect outperforms state-of-the-art model-based iterative reconstruction (MBIR) techniques as well as CNN-based denoising.","PeriodicalId":46807,"journal":{"name":"IEEE Transactions on Radiation and Plasma Medical Sciences","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Uconnect: Synergistic Spectral CT Reconstruction With U-Nets Connecting the Energy Bins\",\"authors\":\"Zhihan Wang;Alexandre Bousse;Franck Vermet;Jacques Froment;Béatrice Vedel;Alessandro Perelli;Jean-Pierre Tasu;Dimitris Visvikis\",\"doi\":\"10.1109/TRPMS.2023.3330045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Spectral computed tomography (CT) offers the possibility to reconstruct attenuation images at different energy levels, which can be then used for material decomposition. However, traditional methods reconstruct each energy bin individually and are vulnerable to noise. In this article, we propose a novel synergistic method for spectral CT reconstruction, namely, Uconnect. It utilizes trained convolutional neural networks (CNNs) to connect the energy bins to a latent image so that the full binned data is used synergistically. We experiment on two types of low-dose data: 1) simulated and 2) real patient data. Qualitative and quantitative analysis show that our proposed Uconnect outperforms state-of-the-art model-based iterative reconstruction (MBIR) techniques as well as CNN-based denoising.\",\"PeriodicalId\":46807,\"journal\":{\"name\":\"IEEE Transactions on Radiation and Plasma Medical Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2023-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Radiation and Plasma Medical Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10308615/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Radiation and Plasma Medical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10308615/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Uconnect: Synergistic Spectral CT Reconstruction With U-Nets Connecting the Energy Bins
Spectral computed tomography (CT) offers the possibility to reconstruct attenuation images at different energy levels, which can be then used for material decomposition. However, traditional methods reconstruct each energy bin individually and are vulnerable to noise. In this article, we propose a novel synergistic method for spectral CT reconstruction, namely, Uconnect. It utilizes trained convolutional neural networks (CNNs) to connect the energy bins to a latent image so that the full binned data is used synergistically. We experiment on two types of low-dose data: 1) simulated and 2) real patient data. Qualitative and quantitative analysis show that our proposed Uconnect outperforms state-of-the-art model-based iterative reconstruction (MBIR) techniques as well as CNN-based denoising.