{"created":"2023-05-15T14:45:50.711912+00:00","id":62632,"links":{},"metadata":{"_buckets":{"deposit":"fd59420d-201f-43ed-b5a0-88be93324ec6"},"_deposit":{"created_by":1,"id":"62632","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"62632"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00062632","sets":["10:29"]},"author_link":["618791","618777","618784","618785","618787","618788","618790","618779","618789","618793","618778","618781","618786","618792","618783","618782","618780"],"item_10005_date_7":{"attribute_name":"発表年月日","attribute_value_mlt":[{"subitem_date_issued_datetime":"2008-06-18","subitem_date_issued_type":"Issued"}]},"item_10005_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Objectives: We propose and evaluate a MAP estimation algorithm in likelihood based graphical analysis (MEGA) to reduce noise-induced bias and variance of the total volume of distribution (VT) using widely used Logan graphical analysis (GA) for neuroreceptor imaging .\nMethods: In MEGA, a set of time-activity curves (TACs) was formed with VT varying in physiological range as a template, and then the most similar TAC was sought out for a given measured TAC in a feature space. In simulation, MEGA were compared with other three methods, GA, Multilinear analysis (MA1), likelihood estimation in GA (LEGA) using 500 noisy TACs under seven physiological conditions (from 9.9 to 61.5 of VT). PET studies of 11C-SA4503, a ligand for 1 receptors, were performed for three normal volunteers at baseline and after loading selective serotonin reuptake inhibitors. In clinical studies, the VT images of estimated by MEGA were compared with ROI estimates by nonlinear least square fitting (NLS) over 4 brain regions.\nResults: In the simulation study, the estimated VT by GA had large underestimation (y = 0.27x + 8.72, r2 = 0.87). Applying the other methods (MA1, LEGA, and MEGA), this bias was improved (y = 0.80x + 4.04, r2 = 0.98; y = 0.85x + 3.05, r2 = 0.99; y = 0.96x + 1.21, r2 = 0.99, respectively). MA1 and LEGA increased variance of the estimated VT in simulation and clinical VT images. However, MEGA improved SNR in VT images with linear correlations between ROI estimates by NLS (y = 0.87x + 5.1, r2 = 0.96).\nConclusion: MEGA could improve estimates of VT in clinical neuroreceptor PET imaging.","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":"55th SNM Annual Meeting","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":"Shidahara, Miho"}],"nameIdentifiers":[{"nameIdentifier":"618777","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Seki, Chie"}],"nameIdentifiers":[{"nameIdentifier":"618778","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Naganawa, Mika"}],"nameIdentifiers":[{"nameIdentifier":"618779","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Sakata, Muneyuki"}],"nameIdentifiers":[{"nameIdentifier":"618780","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Ito, Hiroshi"}],"nameIdentifiers":[{"nameIdentifier":"618781","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kanno, Iwao"}],"nameIdentifiers":[{"nameIdentifier":"618782","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Ishiwata, Kiichi"}],"nameIdentifiers":[{"nameIdentifier":"618783","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kimura, Yuichi"}],"nameIdentifiers":[{"nameIdentifier":"618784","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"et.al"}],"nameIdentifiers":[{"nameIdentifier":"618785","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"志田原 美保","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"618786","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"関 千江","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"618787","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"長縄 美香","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"618788","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"坂田 宗之","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"618789","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"伊藤 浩","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"618790","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"菅野 巖","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"618791","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"石渡 喜一","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"618792","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"木村 裕一","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"618793","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":"MAP Estimation in Logan Graphical Analysis for Neuroreceptor PET Imaging","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"MAP Estimation in Logan Graphical Analysis for Neuroreceptor PET Imaging"}]},"item_type_id":"10005","owner":"1","path":["29"],"pubdate":{"attribute_name":"公開日","attribute_value":"2008-07-08"},"publish_date":"2008-07-08","publish_status":"0","recid":"62632","relation_version_is_last":true,"title":["MAP Estimation in Logan Graphical Analysis for Neuroreceptor PET Imaging"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T21:28:32.815645+00:00"}