{"title":"99m心肌灌注SPECT自动去除心外热点","authors":"W. Tan, G. Coatrieux, R. Besar, B. Solaiman","doi":"10.1109/ICSIPA.2009.5478667","DOIUrl":null,"url":null,"abstract":"In Technitium-99m myocardial perfusion SPECT (MPS) tomograms, there is usually a substantial radioactive tracer uptake in the abdominal organs, especially the liver, bowel and stomach. This extracardiac activity frequently emerges as areas of intense brightness or hotspots, which hamper efforts in automatic MPS quantification. Though it would be favourable to remove the hotspots, their multitudinous appearance and proximity to the heart have made them difficult to be removed. In this paper, we propose an image processing technique to automatically remove the hotspots. Our technique uses the morphological watershed segmentation to delineate the hotspots before they are iteratively removed. The proposed technique has been applied on clinical MPS tomograms in which it has completely removed the hotspots in 90% of the test data. In addition, it has also shown to increase the success rate of an automatic left ventricle detection scheme to 100%.","PeriodicalId":400165,"journal":{"name":"2009 IEEE International Conference on Signal and Image Processing Applications","volume":"38 10S 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automatic removal of extracardiac hotspots in technetium-99m myocardial perfusion SPECT\",\"authors\":\"W. Tan, G. Coatrieux, R. Besar, B. Solaiman\",\"doi\":\"10.1109/ICSIPA.2009.5478667\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In Technitium-99m myocardial perfusion SPECT (MPS) tomograms, there is usually a substantial radioactive tracer uptake in the abdominal organs, especially the liver, bowel and stomach. This extracardiac activity frequently emerges as areas of intense brightness or hotspots, which hamper efforts in automatic MPS quantification. Though it would be favourable to remove the hotspots, their multitudinous appearance and proximity to the heart have made them difficult to be removed. In this paper, we propose an image processing technique to automatically remove the hotspots. Our technique uses the morphological watershed segmentation to delineate the hotspots before they are iteratively removed. The proposed technique has been applied on clinical MPS tomograms in which it has completely removed the hotspots in 90% of the test data. In addition, it has also shown to increase the success rate of an automatic left ventricle detection scheme to 100%.\",\"PeriodicalId\":400165,\"journal\":{\"name\":\"2009 IEEE International Conference on Signal and Image Processing Applications\",\"volume\":\"38 10S 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Conference on Signal and Image Processing Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSIPA.2009.5478667\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Signal and Image Processing Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSIPA.2009.5478667","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic removal of extracardiac hotspots in technetium-99m myocardial perfusion SPECT
In Technitium-99m myocardial perfusion SPECT (MPS) tomograms, there is usually a substantial radioactive tracer uptake in the abdominal organs, especially the liver, bowel and stomach. This extracardiac activity frequently emerges as areas of intense brightness or hotspots, which hamper efforts in automatic MPS quantification. Though it would be favourable to remove the hotspots, their multitudinous appearance and proximity to the heart have made them difficult to be removed. In this paper, we propose an image processing technique to automatically remove the hotspots. Our technique uses the morphological watershed segmentation to delineate the hotspots before they are iteratively removed. The proposed technique has been applied on clinical MPS tomograms in which it has completely removed the hotspots in 90% of the test data. In addition, it has also shown to increase the success rate of an automatic left ventricle detection scheme to 100%.