{"created":"2023-05-15T14:39:21.572173+00:00","id":54061,"links":{},"metadata":{"_buckets":{"deposit":"161a6eed-bf69-41df-b1e5-b57dbabcb28a"},"_deposit":{"created_by":1,"id":"54061","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"54061"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00054061","sets":["2"]},"author_link":["551868","551867","551871","551872","551874","551869","551865","551866","551875","551873","551870","551864"],"item_10003_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2008-09","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"209","bibliographicPageEnd":"31","bibliographicPageStart":"27","bibliographicVolumeNumber":"108","bibliographic_titles":[{"bibliographic_title":"電子情報通信学会技術研究報告"}]}]},"item_10003_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Logan Graphical Analysis (LGA)は、陽電子断層像を用いた神経受容体の画像化において標準的に使用されるアルゴリズムである。LGAは、血中及び組織中の放射能濃度の経時変化の間で成立する直線関係を用いるため、その成立時刻（t*）を指定しなければならないが、雑音を多く含むPETデータにおいて全画素に対するt*の推定は容易ではない。そこで本手法では、適切なt*の下では、直線推定による残差の符号がランダムになることに着目し、ランダム性をノンパラメトリックに判定する連検定を用いてt*を画素毎に決定するアルゴリズムを提案した。その結果、提案手法による神経受容体の推定精度改善が示唆された。","subitem_description_type":"Abstract"}]},"item_10003_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0913-5685","subitem_source_identifier_type":"ISSN"}]},"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":"大柿, 宏人"}],"nameIdentifiers":[{"nameIdentifier":"551864","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"木村, 裕一"}],"nameIdentifiers":[{"nameIdentifier":"551865","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"長縄, 美香"}],"nameIdentifiers":[{"nameIdentifier":"551866","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"坂田, 宗之"}],"nameIdentifiers":[{"nameIdentifier":"551867","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"志田原, 美保"}],"nameIdentifiers":[{"nameIdentifier":"551868","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"菅, 幹生"}],"nameIdentifiers":[{"nameIdentifier":"551869","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"大柿 宏人","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"551870","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"木村 裕一","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"551871","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"長縄 美香","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"551872","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"坂田 宗之","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"551873","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"志田原 美保","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"551874","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"菅 幹生","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"551875","nameIdentifierScheme":"WEKO"}]}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"conference paper","resourceuri":"http://purl.org/coar/resource_type/c_5794"}]},"item_title":"連検定を用いたLogan graphical analysisの開始点決定によるPET神経受容体定量化の精度改善","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"連検定を用いたLogan graphical analysisの開始点決定によるPET神経受容体定量化の精度改善"}]},"item_type_id":"10003","owner":"1","path":["2"],"pubdate":{"attribute_name":"公開日","attribute_value":"2008-10-21"},"publish_date":"2008-10-21","publish_status":"0","recid":"54061","relation_version_is_last":true,"title":["連検定を用いたLogan graphical analysisの開始点決定によるPET神経受容体定量化の精度改善"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T23:05:20.327142+00:00"}