{"created":"2021-03-01T07:06:31.974848+00:00","id":28159,"links":{},"metadata":{"_buckets":{"deposit":"184bb4c3-0607-4c62-8c2b-97a4d3cd0fd3"},"_deposit":{"id":"28159","owners":[],"pid":{"revision_id":0,"type":"depid","value":"28159"},"status":"published"},"_oai":{"id":"oai:tsukuba.repo.nii.ac.jp:00028159","sets":["152:825","3:62:5598:657"]},"item_5_biblio_info_6":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2012-04","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"7","bibliographicPageEnd":"2061","bibliographicPageStart":"2039","bibliographicVolumeNumber":"57","bibliographic_titles":[{"bibliographic_title":"Physics in medicine and biology"}]}]},"item_5_creator_3":{"attribute_name":"著者別名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"工藤, 博幸"}],"nameIdentifiers":[{},{},{}]}]},"item_5_description_4":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"The radiation dose generated from x-ray computed tomography (CT) scans and its responsibility for increasing the risk of malignancy became a major concern in the medical imaging community. Accordingly, investigating possible approaches for image reconstruction from low-dose imaging protocols, which minimize the patient radiation exposure without affecting image quality, has become an issue of interest. Statistical reconstruction (SR) methods are known to achieve a superior image quality compared with conventional analytical methods. Effective physical noise modeling and possibilities of incorporating priors in the image reconstruction problem are the main advantages of the SR methods. Nevertheless, the high computation cost limits its wide use in clinical scanners. This paper presents a framework for SR in x-ray CT when the angular sampling rate of the projection data is low. The proposed framework is based on the fact that, in many CT imaging applications, some physical and anatomical structures and the corresponding attenuation information of the scanned object can be a priori known. Therefore, the x-ray attenuation distribution in some regions of the object can be expected prior to the reconstruction. Under this assumption, the proposed method is developed by incorporating this prior information into the image reconstruction objective function to suppress streak artifacts. We limit the prior information to only a set of intensity values that represent the average intensity of the normal and expected homogeneous regions within the scanned object. This prior information can be easily computed in several x-ray CT applications. Considering the theory of compressed sensing, the objective function is formulated using the ℓ1 norm distance between the reconstructed image and the available intensity priors. Experimental comparative studies applied to simulated data and real data are used to evaluate the proposed method. The comparison indicates a significant improvement in image quality when the proposed method is used.","subitem_description_type":"Abstract"}]},"item_5_identifier_34":{"attribute_name":"URI","attribute_value_mlt":[{"subitem_identifier_type":"HDL","subitem_identifier_uri":"http://hdl.handle.net/2241/118268"}]},"item_5_publisher_27":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"IOP Publishing"}]},"item_5_relation_10":{"attribute_name":"PubMed番号","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"22430037","subitem_relation_type_select":"PMID"}}]},"item_5_relation_11":{"attribute_name":"DOI","attribute_value_mlt":[{"subitem_relation_type_id":{"subitem_relation_type_id_text":"10.1088/0031-9155/57/7/2039","subitem_relation_type_select":"DOI"}}]},"item_5_rights_12":{"attribute_name":"権利","attribute_value_mlt":[{"subitem_rights":"© 2012 Institute of Physics and Engineering in Medicine　\nThis is an author-created, un-copyedited version of an article accepted for publication in Physics in medicine and biology. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at 10.1088/0031-9155/57/7/2039"}]},"item_5_select_15":{"attribute_name":"著者版フラグ","attribute_value_mlt":[{"subitem_select_item":"author"}]},"item_5_source_id_7":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"0031-9155","subitem_source_identifier_type":"ISSN"}]},"item_5_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA00774048","subitem_source_identifier_type":"NCID"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Rashed, Essam A"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kudo, Hiroyuki"}],"nameIdentifiers":[{}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2013-12-25"}],"displaytype":"detail","filename":"PMB_57-7.pdf","filesize":[{"value":"3.3 MB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"PMB_57-7.pdf","url":"https://tsukuba.repo.nii.ac.jp/record/28159/files/PMB_57-7.pdf"},"version_id":"ed077649-b84e-470f-ae5b-da9486a043ba"}]},"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":"Statistical image reconstruction from limited projection data with intensity prior","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Statistical image reconstruction from limited projection data with intensity prior"}]},"item_type_id":"5","owner":"1","path":["825","657"],"pubdate":{"attribute_name":"公開日","attribute_value":"2013-01-30"},"publish_date":"2013-01-30","publish_status":"0","recid":"28159","relation_version_is_last":true,"title":["Statistical image reconstruction from limited projection data with intensity prior"],"weko_creator_id":"1","weko_shared_id":5},"updated":"2022-04-27T08:56:07.127612+00:00"}