{"created":"2023-05-15T14:50:57.565118+00:00","id":69660,"links":{},"metadata":{"_buckets":{"deposit":"1cdc0c2b-1f61-47d3-821b-dc4c27adeb72"},"_deposit":{"created_by":1,"id":"69660","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"69660"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00069660","sets":["10:28"]},"author_link":["683803","683812","683817","683813","683810","683802","683814","683811","683804","683801","683809","683815","683807","683805","683806","683808","683816"],"item_10005_date_7":{"attribute_name":"発表年月日","attribute_value_mlt":[{"subitem_date_issued_datetime":"2008-12-15","subitem_date_issued_type":"Issued"}]},"item_10005_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Objective:  Logan graphical analysis (LGA; Logan, JCBF, 1990) is useful for neuroreceptor imaging. LGA does not assume the number of compartments to describe a behavior of administered radioligand, and it is implemented as a line estimation, that is stable and fast. However, LGA with regression line estimation suffers from an underestimation in estimated total distribution volume (VT) due to a noise in PET data (tTAC) (Kimura, ANM, 2007). In this study, a new approach combining a likelihood estimation for graphical analysis (LEGA; Ogden, Stat Med, 2003) with a maximum a posteriori (MAP) algorithm, naming MEGA (MAP estimation in graphical analysis). \nAlgorithm: In LEGA, tTAC is computed from VT and an intercept in LGA (b) in the manner of LGA, and VT and b are derived in a likelihood fashion using a nonlinear estimation algorithm. In MEGA,  a set of tTACs are formed with VT and b varying in a physiological range as a prior knowledge, and then the most similar tTAC is searched for a given measured tTAC in a feature space, where a shape of tTAC is represented as a point in a multidimensional space, and a similarity between two tTACs can be evaluated using the distance as described in Kimura (NeuroImage, 1999). A noise in a measured voxel-based tTAC should be addressed. A fluctuation in a shape of tTAC is dealt with a distribution of feature points, and a Mahalanobis distance is utilized for the. Moreover, for noise reduction in a tTAC, principal components (PCs) are computed from the template to form an orthogonal subspace, and a measured tTAC is projected onto it before the searching.\nConclusion: LGA is attractive for PET neuroreceptor imaging due to its reliability and simplicity. But the noise-induced underestimation is problematic. The proposed MEGA realized robust estimation and suppressed the bias under existence of large noise observed in a voxel-based tTAC. In conclusion, MEGA can be practical algorithm for LGA neuroreceptor imaging with PET.","subitem_description_type":"Abstract"}]},"item_10005_description_6":{"attribute_name":"会議概要（会議名, 開催地, 会期, 主催者等）","attribute_value_mlt":[{"subitem_description":"分子イメージング研究シンポジウム　MOLECULAR IMAGING2008","subitem_description_type":"Other"}]},"item_access_right":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"metadata only access","subitem_access_right_uri":"http://purl.org/coar/access_right/c_14cb"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Kimura, Yuichi"}],"nameIdentifiers":[{"nameIdentifier":"683801","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Shidahara, Miho"}],"nameIdentifiers":[{"nameIdentifier":"683802","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Seki, Chie"}],"nameIdentifiers":[{"nameIdentifier":"683803","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Naganawa, Mika"}],"nameIdentifiers":[{"nameIdentifier":"683804","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Sakata, Muneyuki"}],"nameIdentifiers":[{"nameIdentifier":"683805","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Ishikawa, Masatomo"}],"nameIdentifiers":[{"nameIdentifier":"683806","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Ito, Hiroshi"}],"nameIdentifiers":[{"nameIdentifier":"683807","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kanno, Iwao"}],"nameIdentifiers":[{"nameIdentifier":"683808","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Ishiwata, Kiichi"}],"nameIdentifiers":[{"nameIdentifier":"683809","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"木村 裕一","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"683810","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"志田原 美保","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"683811","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"関 千江","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"683812","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"長縄 美香","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"683813","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"坂田 宗之","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"683814","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"伊藤 浩","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"683815","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"菅野 巖","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"683816","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"石渡 喜一","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"683817","nameIdentifierScheme":"WEKO"}]}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"conference object","resourceuri":"http://purl.org/coar/resource_type/c_c94f"}]},"item_title":"Improvement of likelihood estimation in Logan graphical analysis using maximum a posteriori for neuroreceptor PET imaging --- toward bias-free distribution volume imaging from noise in PET data ---","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Improvement of likelihood estimation in Logan graphical analysis using maximum a posteriori for neuroreceptor PET imaging --- toward bias-free distribution volume imaging from noise in PET data ---"}]},"item_type_id":"10005","owner":"1","path":["28"],"pubdate":{"attribute_name":"公開日","attribute_value":"2008-12-19"},"publish_date":"2008-12-19","publish_status":"0","recid":"69660","relation_version_is_last":true,"title":["Improvement of likelihood estimation in Logan graphical analysis using maximum a posteriori for neuroreceptor PET imaging --- toward bias-free distribution volume imaging from noise in PET data ---"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T20:11:53.442911+00:00"}