{"title":"Scattering correction based on regularization de-convolution for Cone-Beam CT","authors":"Shipeng Xie, Rui-ju Yan","doi":"10.18178/wcse.2016.06.082","DOIUrl":null,"url":null,"abstract":"In Cone-Beam CT (CBCT) imaging systems, the scattering phenomenon has a significant impact on the reconstructed image and is a long-lasting research topic on CBCT. In this paper, we propose a simple, novel and fast approach for mitigating scatter artifacts and increasing the image contrast in CBCT, belonging to the category of convolution-based method in which the projected data is de-convolved with a convolution kernel. A key step in this method is how to determine the convolution kernel. Compared with existing methods, the estimation of convolution kernel is based on bi-l1-l2-norm regularization imposed on both the intermediate the known scatter contaminated projection images and the convolution kernel. Our approach can reduce the scatter artifacts from 12.930 to 2.133.","PeriodicalId":8462,"journal":{"name":"arXiv: Medical Physics","volume":"56 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2016-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv: Medical Physics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18178/wcse.2016.06.082","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In Cone-Beam CT (CBCT) imaging systems, the scattering phenomenon has a significant impact on the reconstructed image and is a long-lasting research topic on CBCT. In this paper, we propose a simple, novel and fast approach for mitigating scatter artifacts and increasing the image contrast in CBCT, belonging to the category of convolution-based method in which the projected data is de-convolved with a convolution kernel. A key step in this method is how to determine the convolution kernel. Compared with existing methods, the estimation of convolution kernel is based on bi-l1-l2-norm regularization imposed on both the intermediate the known scatter contaminated projection images and the convolution kernel. Our approach can reduce the scatter artifacts from 12.930 to 2.133.