{"created":"2023-05-15T14:58:11.753088+00:00","id":78935,"links":{},"metadata":{"_buckets":{"deposit":"c9751041-88eb-46ea-9ffd-34aae978a542"},"_deposit":{"created_by":1,"id":"78935","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"78935"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00078935","sets":["1"]},"author_link":["1004802","1004805","1004800","1004804","1004801","1004803"],"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-02","bibliographicIssueDateType":"Issued"},"bibliographicPageEnd":"205","bibliographicPageStart":"196","bibliographicVolumeNumber":"70","bibliographic_titles":[{"bibliographic_title":"Physica Medica"}]}]},"item_8_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Purpose: We have developed a new method to track tumor position using fluoroscopic images, and evaluated it using hepatocellular carcinoma case data.\nMethods: Our method consists of a training stage and a tracking stage. In the training stage, the model data for the positional relationship between the diaphragm and the tumor are calculated using four-dimensional com- puted tomography (4DCT) data. The diaphragm is detected along a straight line, which was chosen to avoid 4DCT artifact. In the tracking stage, the tumor position on the fluoroscopic images is calculated by applying the model to the diaphragm. Using data from seven liver cases, we evaluated four metrics: diaphragm edge detection error, modeling error, patient setup error, and tumor tracking error. We measured tumor tracking error for the 15 fluoroscopic sequences from the cases and recorded the computation time.\nResults: The mean positional error in diaphragm tracking was 0.57 ± 0.62 mm. The mean positional error in tumor tracking in three-dimensional (3D) space was 0.63 ± 0.30 mm by modeling error, and 0.81–2.37 mm with 1–2 mm setup error. The mean positional error in tumor tracking in the fluoroscopy sequences was 1.30 ± 0.54 mm and the mean computation time was 69.0 ± 4.6 ms and 23.2 ± 1.3 ms per frame for the training and tracking stages, respectively.\nConclusions: Our markerless tracking method successfully estimated tumor positions. We believe our results will be useful in increasing treatment accuracy for liver cases.","subitem_description_type":"Abstract"}]},"item_8_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Elsevier"}]},"item_8_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1016/j.ejmp.2020.02.001","subitem_relation_type_select":"DOI"}}]},"item_8_relation_17":{"attribute_name":"関連サイト","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"https://www.sciencedirect.com/science/article/abs/pii/S1120179720300326","subitem_relation_type_select":"URI"}}]},"item_8_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"1120-1797","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":"Hirai, Ryusuke"}],"nameIdentifiers":[{"nameIdentifier":"1004800","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Sakata, Yukinobu"}],"nameIdentifiers":[{"nameIdentifier":"1004801","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Mori, Shinichiro"}],"nameIdentifiers":[{"nameIdentifier":"1004802","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Ryusuke, Hirai","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"1004803","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Yukinobu, Sakata","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"1004804","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Shinichiro, Mori","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"1004805","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":"journal article","resourceuri":"http://purl.org/coar/resource_type/c_6501"}]},"item_title":"Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma"}]},"item_type_id":"8","owner":"1","path":["1"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-02-10"},"publish_date":"2020-02-10","publish_status":"0","recid":"78935","relation_version_is_last":true,"title":["Regression model-based real-time markerless tumor tracking with fluoroscopic images for hepatocellular carcinoma"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T19:25:50.991375+00:00"}