{"title":"扇形波束几何中正反投影的卷积框架","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":"{\"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}","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}
A Convolutional Framework for Forward and Back-Projection in Fan-Beam Geometry
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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