{"title":"非局部功能的低剂量CT重建","authors":"Ryosuke Ueda, H. Kudo","doi":"10.1145/3387168.3387248","DOIUrl":null,"url":null,"abstract":"In medical CT, the X-ray exposure dose reduction is expected. As a decrease in the dose, the image is degraded due to the noise. Therefore, the development of the noise reduction algorithm while maintaining image quality is an important issue. To suppress the noise, the penalized least squares method is effective. Recently, non-local total variation (NLTV) and non-local structure tensor TV (NLSTV) have been reported. These functional penalties have shown excellent denoising performance of the natural image. In this paper, we apply the functionals to the low-dose CT reconstruction problem. The reconstruction method and the comparison between TV, NLTV, and NLSTV are shown.","PeriodicalId":346739,"journal":{"name":"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Low-Dose CT Reconstruction with Non-Local Functionals\",\"authors\":\"Ryosuke Ueda, H. Kudo\",\"doi\":\"10.1145/3387168.3387248\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In medical CT, the X-ray exposure dose reduction is expected. As a decrease in the dose, the image is degraded due to the noise. Therefore, the development of the noise reduction algorithm while maintaining image quality is an important issue. To suppress the noise, the penalized least squares method is effective. Recently, non-local total variation (NLTV) and non-local structure tensor TV (NLSTV) have been reported. These functional penalties have shown excellent denoising performance of the natural image. In this paper, we apply the functionals to the low-dose CT reconstruction problem. The reconstruction method and the comparison between TV, NLTV, and NLSTV are shown.\",\"PeriodicalId\":346739,\"journal\":{\"name\":\"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3387168.3387248\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd International Conference on Vision, Image and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3387168.3387248","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Low-Dose CT Reconstruction with Non-Local Functionals
In medical CT, the X-ray exposure dose reduction is expected. As a decrease in the dose, the image is degraded due to the noise. Therefore, the development of the noise reduction algorithm while maintaining image quality is an important issue. To suppress the noise, the penalized least squares method is effective. Recently, non-local total variation (NLTV) and non-local structure tensor TV (NLSTV) have been reported. These functional penalties have shown excellent denoising performance of the natural image. In this paper, we apply the functionals to the low-dose CT reconstruction problem. The reconstruction method and the comparison between TV, NLTV, and NLSTV are shown.