{"created":"2023-05-15T15:01:58.391467+00:00","id":84057,"links":{},"metadata":{"_buckets":{"deposit":"3c1a86b7-96be-4ab9-81f9-745ba6037e0a"},"_deposit":{"created_by":1,"id":"84057","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"84057"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00084057","sets":["1"]},"author_link":["1015167","1015165","1015164","1015169","1015170","1015171","1015166","1015168"],"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2021-06","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"7","bibliographicPageStart":"074801","bibliographicVolumeNumber":"90","bibliographic_titles":[{"bibliographic_title":"Journal of the Physical Society of Japan"}]}]},"item_8_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"This study presents, for the first time, a method to indirectly estimate the cone-beam computed tomography (CBCT) x-ray spectrum in the diagnostic energy range from the percentage depth dose (PDD) using machine learning (ML) algorithms. Assuming that the measured PDD is a weighted mean of monochromatic PDDs (mPDDs) resulting from monochromatic x-ray energies, mPDDs from the diagnostic energy range of 10 to 140 keV are simulated at 1 keV intervals by Monte Carlo (MC) calculation. Then, x-ray spectrum prediction models are constructed using two different ML approaches, namely the artificial neural network (ANN) based on a generative model and a maximum a posterior (MAP) model. Both models account for more than 80% of the x-ray photons obtained by full MC simulations in commercial CBCT systems. The present method is expected to be applied into a beam hardening reduction in CBCT reconstruction, CBCT dose calculation, and a material decomposition which require exact information on the x-ray energy spectrum.","subitem_description_type":"Abstract"}]},"item_8_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"The Physical Society of Japan"}]},"item_8_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.7566/JPSJ.90.074801","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://journals.jps.jp/doi/full/10.7566/JPSJ.90.074801","subitem_relation_type_select":"URI"}}]},"item_8_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0031-9015","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":"Hasegawa, Yu"}],"nameIdentifiers":[{"nameIdentifier":"1015164","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Akihiro Haga"}],"nameIdentifiers":[{"nameIdentifier":"1015165","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Dousatsu Sakata"}],"nameIdentifiers":[{"nameIdentifier":"1015166","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Yuki Kanazawa"}],"nameIdentifiers":[{"nameIdentifier":"1015167","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Masahide Tominaga"}],"nameIdentifiers":[{"nameIdentifier":"1015168","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Motoharu Sasaki"}],"nameIdentifiers":[{"nameIdentifier":"1015169","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Toshikazu Imae"}],"nameIdentifiers":[{"nameIdentifier":"1015170","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Dousatsu, Sakata","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"1015171","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":"Estimation of X-ray Energy Spectrum of Cone-Beam Computed Tomography Scanner Using Percentage Depth Dose Measurements and Machine Learning Approach","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Estimation of X-ray Energy Spectrum of Cone-Beam Computed Tomography Scanner Using Percentage Depth Dose Measurements and Machine Learning Approach"}]},"item_type_id":"8","owner":"1","path":["1"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-08-03"},"publish_date":"2021-08-03","publish_status":"0","recid":"84057","relation_version_is_last":true,"title":["Estimation of X-ray Energy Spectrum of Cone-Beam Computed Tomography Scanner Using Percentage Depth Dose Measurements and Machine Learning Approach"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T18:44:11.810119+00:00"}