{"title":"A Convolutional Framework for Forward and Back-Projection in Fan-Beam Geometry","authors":"Kai Zhang, A. Entezari","doi":"10.1109/ISBI.2019.8759285","DOIUrl":null,"url":null,"abstract":"We present a convolutional spline framework for highly efficient and accurate computation of forward model for image reconstruction in fan-beam geometry in X-ray computed tomography. The efficiency of computations makes this framework suitable for large-scale optimization algorithms with on-the-fly, memory-less, computations of the forward and back-projection. Our experiments demonstrate the improvements in accuracy as well as efficiency of our model, specifically for first-order box splines (i.e., pixel-basis) compared to recently developed methods for this purpose, namely Look-up Table-based Ray Integration (LTRI) and Separable Footprints (SF) in 2-D.","PeriodicalId":119935,"journal":{"name":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","volume":"388 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI.2019.8759285","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
We present a convolutional spline framework for highly efficient and accurate computation of forward model for image reconstruction in fan-beam geometry in X-ray computed tomography. The efficiency of computations makes this framework suitable for large-scale optimization algorithms with on-the-fly, memory-less, computations of the forward and back-projection. Our experiments demonstrate the improvements in accuracy as well as efficiency of our model, specifically for first-order box splines (i.e., pixel-basis) compared to recently developed methods for this purpose, namely Look-up Table-based Ray Integration (LTRI) and Separable Footprints (SF) in 2-D.
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