{"title":"Local-Limited-Angle CT image reconstruction based on wavelet and Radon domain inpainting","authors":"Ke-jun Wang, Huizhu Ma","doi":"10.1109/ICOSP.2012.6491599","DOIUrl":null,"url":null,"abstract":"X-ray computed tomography (CT) has been playing an important role in diagnostic and industrial fields. However, high imaging dose is harmful to healthy organs. In this paper, a local Reconstruction method based on wavelet and Radon domain inpainting is proposed to recover a small region of interest (ROI) inside a large object only using the Local-Limited-Angle projections. Our method is motivated by the localization in time-frequency domain of wavelet transform and the minimization of the image total variation (TV). The proposed model enhances the resolution of the CT image with filling wavelet coefficients in the Radon domain. Numerical demonstrations of our algorithm are performed with various insufficient parallel beam projections.","PeriodicalId":143331,"journal":{"name":"2012 IEEE 11th International Conference on Signal Processing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 11th International Conference on Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOSP.2012.6491599","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
X-ray computed tomography (CT) has been playing an important role in diagnostic and industrial fields. However, high imaging dose is harmful to healthy organs. In this paper, a local Reconstruction method based on wavelet and Radon domain inpainting is proposed to recover a small region of interest (ROI) inside a large object only using the Local-Limited-Angle projections. Our method is motivated by the localization in time-frequency domain of wavelet transform and the minimization of the image total variation (TV). The proposed model enhances the resolution of the CT image with filling wavelet coefficients in the Radon domain. Numerical demonstrations of our algorithm are performed with various insufficient parallel beam projections.