动态心脏PET数据中功能成分的ICA分离

2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515) Pub Date : 2003-10-19 DOI:10.1109/NSSMIC.2003.1352426

M. Magadán-Méndez, A. Kivimáki, U. Ruotsalainen

{"title":"动态心脏PET数据中功能成分的ICA分离","authors":"M. Magadán-Méndez, A. Kivimáki, U. Ruotsalainen","doi":"10.1109/NSSMIC.2003.1352426","DOIUrl":null,"url":null,"abstract":"The aim of this study was to improve detection of different heart tissues, and specially their boundaries, in H/sub 2//sup 1 5/O PET (positron emission tomography) heart images. This problem was considered as a blind source separation problem. In order to solve it we applied ICA (independent component analysis) on dynamic image data and measured projection profiles (sinograms). The testing was based on two kinds of data: a simple dynamic numerical phantom and human heart data acquired during resting state. The sensitivity of ICA to noise was examined on phantom data, where ICA seemed to be less sensitive to noise on sinogram data than on image data. On cardiac rest data, the results were in line with the results on phantom data.","PeriodicalId":186175,"journal":{"name":"2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"ICA separation of functional components from dynamic cardiac PET data\",\"authors\":\"M. Magadán-Méndez, A. Kivimáki, U. Ruotsalainen\",\"doi\":\"10.1109/NSSMIC.2003.1352426\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The aim of this study was to improve detection of different heart tissues, and specially their boundaries, in H/sub 2//sup 1 5/O PET (positron emission tomography) heart images. This problem was considered as a blind source separation problem. In order to solve it we applied ICA (independent component analysis) on dynamic image data and measured projection profiles (sinograms). The testing was based on two kinds of data: a simple dynamic numerical phantom and human heart data acquired during resting state. The sensitivity of ICA to noise was examined on phantom data, where ICA seemed to be less sensitive to noise on sinogram data than on image data. On cardiac rest data, the results were in line with the results on phantom data.\",\"PeriodicalId\":186175,\"journal\":{\"name\":\"2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515)\",\"volume\":\"14 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-10-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NSSMIC.2003.1352426\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NSSMIC.2003.1352426","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 15

摘要

本研究的目的是提高在H/sub //sup 15 /O PET(正电子发射断层扫描)心脏图像中不同心脏组织的检测，特别是它们的边界。该问题被认为是一个盲源分离问题。为了解决这一问题，我们将ICA(独立分量分析)应用于动态图像数据和测量的投影轮廓(sinogram)。测试基于两种数据:简单的动态数值模拟和静息状态下的人体心脏数据。ICA对噪声的敏感性在幻象数据上进行了检查，其中ICA对正弦图数据的噪声似乎比图像数据的噪声更不敏感。在心脏休息数据上，结果与幻影数据的结果一致。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

ICA separation of functional components from dynamic cardiac PET data

The aim of this study was to improve detection of different heart tissues, and specially their boundaries, in H/sub 2//sup 1 5/O PET (positron emission tomography) heart images. This problem was considered as a blind source separation problem. In order to solve it we applied ICA (independent component analysis) on dynamic image data and measured projection profiles (sinograms). The testing was based on two kinds of data: a simple dynamic numerical phantom and human heart data acquired during resting state. The sensitivity of ICA to noise was examined on phantom data, where ICA seemed to be less sensitive to noise on sinogram data than on image data. On cardiac rest data, the results were in line with the results on phantom data.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

2003 IEEE Nuclear Science Symposium. Conference Record (IEEE Cat. No.03CH37515)

自引率

0.00%

发文量