{"title":"MLAA和MLACF的缺陷","authors":"K. Salvo, M. Defrise","doi":"10.1109/NSSMIC.2016.8069555","DOIUrl":null,"url":null,"abstract":"In time-of-flight (TOF) positron emission tomography (PET) imaging, it is possible to correct for attenuation using only the TOF-PET data. Currently two main iterative algorithms exist for this purpose: MLAA [1] and MLACF [2]. In addition to reconstructing the activity image A, MLAA reconstructs the attenuation map μ, whereas MLACF reconstructs the attenuation correction factors a. While implementing MLAA and MLACF, one has to be careful. Possible pitfalls are: (i) obtaining slice dependent scale factors, (ii) converging to local maxima, (iii) obtaining unbounded estimates, (iv) dividing by zero, and (v) using an incorrect or inefficient update scheme. First we will summarize and expand some previous results of MLAA & MLACF convergence and uniqueness issues. Next we will use this knowledge to understand the pitfalls, while giving details on the implementation to avoid them.","PeriodicalId":184587,"journal":{"name":"2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Pitfalls in MLAA and MLACF\",\"authors\":\"K. Salvo, M. Defrise\",\"doi\":\"10.1109/NSSMIC.2016.8069555\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In time-of-flight (TOF) positron emission tomography (PET) imaging, it is possible to correct for attenuation using only the TOF-PET data. Currently two main iterative algorithms exist for this purpose: MLAA [1] and MLACF [2]. In addition to reconstructing the activity image A, MLAA reconstructs the attenuation map μ, whereas MLACF reconstructs the attenuation correction factors a. While implementing MLAA and MLACF, one has to be careful. Possible pitfalls are: (i) obtaining slice dependent scale factors, (ii) converging to local maxima, (iii) obtaining unbounded estimates, (iv) dividing by zero, and (v) using an incorrect or inefficient update scheme. First we will summarize and expand some previous results of MLAA & MLACF convergence and uniqueness issues. Next we will use this knowledge to understand the pitfalls, while giving details on the implementation to avoid them.\",\"PeriodicalId\":184587,\"journal\":{\"name\":\"2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD)\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NSSMIC.2016.8069555\",\"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 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop (NSS/MIC/RTSD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NSSMIC.2016.8069555","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In time-of-flight (TOF) positron emission tomography (PET) imaging, it is possible to correct for attenuation using only the TOF-PET data. Currently two main iterative algorithms exist for this purpose: MLAA [1] and MLACF [2]. In addition to reconstructing the activity image A, MLAA reconstructs the attenuation map μ, whereas MLACF reconstructs the attenuation correction factors a. While implementing MLAA and MLACF, one has to be careful. Possible pitfalls are: (i) obtaining slice dependent scale factors, (ii) converging to local maxima, (iii) obtaining unbounded estimates, (iv) dividing by zero, and (v) using an incorrect or inefficient update scheme. First we will summarize and expand some previous results of MLAA & MLACF convergence and uniqueness issues. Next we will use this knowledge to understand the pitfalls, while giving details on the implementation to avoid them.
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