{"created":"2023-05-15T14:58:57.035276+00:00","id":79976,"links":{},"metadata":{"_buckets":{"deposit":"5390ee79-e74c-4d5c-8ab3-6da8ff6f537b"},"_deposit":{"created_by":1,"id":"79976","owners":[1],"pid":{"revision_id":0,"type":"depid","value":"79976"},"status":"published"},"_oai":{"id":"oai:repo.qst.go.jp:00079976","sets":["1"]},"author_link":["883049","883043","883048","883051","883045","883044","883050","883046","883047"],"item_8_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2020-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"8","bibliographicPageEnd":"3532","bibliographicPageStart":"3520","bibliographicVolumeNumber":"47","bibliographic_titles":[{"bibliographic_title":"Medical Physics"}]}]},"item_8_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Imaging of the secondary electron bremsstrahlung (SEB) X-rays emitted during particle-ion irradiation is a promising method for beam range estimation. However, the SEB X-ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the X-ray camera and low-count situation may impede correctly estimating the beam range and width in SEB X-ray images. To overcome these limitations of the SEB X-ray images measured by the X-ray camera, a deep learning (DL) approach was proposed in this work to predict the dose images for estimating the range and width of the carbon-ion beam on the measured SEB X-ray images. To prepare enough data for the DL training efficiently, 10,000 simulated SEB X-ray and dose image pairs were generated by our in-house developed model function for different carbon-ion beam energies and doses. The proposed DL neural network consists of two U-nets for SEB X-ray to dose image conversion and super-resolution. After the network being trained with these simulated X-ray and dose image pairs, the dose images were predicted from simulated and measured SEB X-ray testing images for performance evaluation. For the 500 simulated testing images, the average mean squared error (MSE) was 2.5 × 10^-5 and average structural similarity index (SSIM) was 0.997 while the error of both beam range and width was within 1 mm FWHM. For the three measured SEB X-ray images, the MSE was no worse than 5.5 × 10^-3 and SSIM was no worse than 0.980 while the error of the beam range and width was 2 mm and 5 mm FWHM, respectively.  We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach. ","subitem_description_type":"Abstract"}]},"item_8_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"Wiley"}]},"item_8_relation_14":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1002/mp.14205","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://doi.org/10.1002/mp.14205","subitem_relation_type_select":"DOI"}}]},"item_8_source_id_9":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0094-2405","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":"Yamaguchi, Mitsutaka"}],"nameIdentifiers":[{"nameIdentifier":"883043","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Chih-Chieh, Liu (UC Davis)"}],"nameIdentifiers":[{"nameIdentifier":"883044","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Hsuan-Ming, Huang (National Taiwan Univ.)"}],"nameIdentifiers":[{"nameIdentifier":"883045","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Takuya, Yabe (Nagoya Univ.)"}],"nameIdentifiers":[{"nameIdentifier":"883046","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Takashi, Akagi (Hyogo Ion Beam Medical Center)"}],"nameIdentifiers":[{"nameIdentifier":"883047","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kawachi, Naoki"}],"nameIdentifiers":[{"nameIdentifier":"883048","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Seiichi, Yamamoto (Nagoya Univ.)"}],"nameIdentifiers":[{"nameIdentifier":"883049","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Yamaguchi, Mitsutaka","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"883050","nameIdentifierScheme":"WEKO"}]},{"creatorNames":[{"creatorName":"Kawachi, Naoki","creatorNameLang":"en"}],"nameIdentifiers":[{"nameIdentifier":"883051","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":"Dose image prediction for range and width verifications from carbon-ion induced secondary electron bremsstrahlung X-rays using deep learning workflow","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Dose image prediction for range and width verifications from carbon-ion induced secondary electron bremsstrahlung X-rays using deep learning workflow"}]},"item_type_id":"8","owner":"1","path":["1"],"pubdate":{"attribute_name":"公開日","attribute_value":"2020-04-21"},"publish_date":"2020-04-21","publish_status":"0","recid":"79976","relation_version_is_last":true,"title":["Dose image prediction for range and width verifications from carbon-ion induced secondary electron bremsstrahlung X-rays using deep learning workflow"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2023-05-15T21:38:13.162564+00:00"}