{"title":"幅度共振成像数据的非线性空间域一阶矩估计","authors":"K. Sim, C. Toa","doi":"10.1109/ICORAS.2016.7872627","DOIUrl":null,"url":null,"abstract":"A new method for signal estimation in the magnitude resonance imaging (MRI) which follows Rician distribution data is proposed. Sigma estimation in the MRI is importance for the various post-processing tasks. Although different methods for sigma estimation of MRI are available, most of these methods require multiple images. In this paper, Nonlinear spatial domain first order moment (NSDFOM) estimator technique is focused. This estimator is then compared with other estimators in terms of the sigma estimation which used only a single image. The experimental result shows that NSDFOM method able to generate more accurate sigma estimation.","PeriodicalId":393534,"journal":{"name":"2016 International Conference on Robotics, Automation and Sciences (ICORAS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Nonlinear spatial domain first order moment estimation in magnitude resonance imaging data\",\"authors\":\"K. Sim, C. Toa\",\"doi\":\"10.1109/ICORAS.2016.7872627\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new method for signal estimation in the magnitude resonance imaging (MRI) which follows Rician distribution data is proposed. Sigma estimation in the MRI is importance for the various post-processing tasks. Although different methods for sigma estimation of MRI are available, most of these methods require multiple images. In this paper, Nonlinear spatial domain first order moment (NSDFOM) estimator technique is focused. This estimator is then compared with other estimators in terms of the sigma estimation which used only a single image. The experimental result shows that NSDFOM method able to generate more accurate sigma estimation.\",\"PeriodicalId\":393534,\"journal\":{\"name\":\"2016 International Conference on Robotics, Automation and Sciences (ICORAS)\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 International Conference on Robotics, Automation and Sciences (ICORAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICORAS.2016.7872627\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Robotics, Automation and Sciences (ICORAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICORAS.2016.7872627","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nonlinear spatial domain first order moment estimation in magnitude resonance imaging data
A new method for signal estimation in the magnitude resonance imaging (MRI) which follows Rician distribution data is proposed. Sigma estimation in the MRI is importance for the various post-processing tasks. Although different methods for sigma estimation of MRI are available, most of these methods require multiple images. In this paper, Nonlinear spatial domain first order moment (NSDFOM) estimator technique is focused. This estimator is then compared with other estimators in terms of the sigma estimation which used only a single image. The experimental result shows that NSDFOM method able to generate more accurate sigma estimation.