Daniel Tenbrinck, M. Dawood, F. Gigengack, M. Fieseler, Xiaoyi Jiang, Klaus Schiifers
{"title":"Motion correction in Positron Emission Tomography considering Partial Volume Effects in optical flow estimation","authors":"Daniel Tenbrinck, M. Dawood, F. Gigengack, M. Fieseler, Xiaoyi Jiang, Klaus Schiifers","doi":"10.1109/ISBI.2010.5490218","DOIUrl":null,"url":null,"abstract":"Motion correction in Positron Emission Tomography (PET) using optical flow estimation can lead to image artifacts due to Partial Volume Effects (PVE). These artifacts appear especially in cardiac gated PET images and cause blurred edges in the averaged gates. In this paper we propose a new method to motion correct PET images considering the PVE during optical flow estimation. For this purpose we introduce a local intensity correction algorithm and combine it with the optical flow computation in an iterative scheme. The results of our approach show a qualitative and quantitative improvement of the motion corrected PET gates in examinations of both human patients and laboratory mice for pre-clinical research.","PeriodicalId":250523,"journal":{"name":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","volume":"70 4","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISBI.2010.5490218","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
Motion correction in Positron Emission Tomography (PET) using optical flow estimation can lead to image artifacts due to Partial Volume Effects (PVE). These artifacts appear especially in cardiac gated PET images and cause blurred edges in the averaged gates. In this paper we propose a new method to motion correct PET images considering the PVE during optical flow estimation. For this purpose we introduce a local intensity correction algorithm and combine it with the optical flow computation in an iterative scheme. The results of our approach show a qualitative and quantitative improvement of the motion corrected PET gates in examinations of both human patients and laboratory mice for pre-clinical research.