{"created":"2025-03-07T01:08:33.332949+00:00","id":2012824,"links":{},"metadata":{"_buckets":{"deposit":"8a0571ba-ea88-4bea-9ee4-8b9c4c5a8eea"},"_deposit":{"created_by":51,"id":"2012824","owner":"51","owners":[51],"pid":{"revision_id":0,"type":"depid","value":"2012824"},"status":"published"},"_oai":{"id":"oai:tokushima-u.repo.nii.ac.jp:02012824","sets":["1713853213384:1713853295607"]},"author_link":["806","1587"],"control_number":"2012824","item_10001_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2024-07-23","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"8","bibliographicPageStart":"178","bibliographicVolumeNumber":"10","bibliographic_titles":[{"bibliographic_title":"Journal of Imaging","bibliographic_titleLang":"en"}]}]},"item_10001_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Computed tomography (CT) imaging plays a crucial role in various medical applications, but noise in projection data can significantly degrade image quality and hinder diagnosis accuracy. Iterative algorithms for tomographic image reconstruction outperform transform methods, especially in scenarios with severe noise in projections. In this paper, we propose a method to dynamically adjust two parameters included in the iterative rules during the reconstruction process. The algorithm, named the parameter-extended expectation-maximization based on power divergence (PXEM), aims to minimize the weighted extended power divergence between the measured and forward projections at each iteration. Our numerical and physical experiments showed that PXEM surpassed conventional methods such as maximum-likelihood expectation-maximization (MLEM), particularly in noisy scenarios. PXEM combines the noise suppression capabilities of power divergence-based expectation-maximization with static parameters at every iteration and the edge preservation properties of MLEM. The experimental results demonstrated significant improvements in image quality in metrics such as the structural similarity index measure and peak signal-to-noise ratio. PXEM improves CT image reconstruction quality under high noise conditions through enhanced optimization techniques.","subitem_description_language":"en","subitem_description_type":"Abstract"}]},"item_10001_publisher_8":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"MDPI","subitem_publisher_language":"en"}]},"item_10001_rights_15":{"attribute_name":"権利情報","attribute_value_mlt":[{"subitem_rights":"This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).","subitem_rights_language":"en"}]},"item_10001_source_id_9":{"attribute_name":"収録物ID","attribute_value_mlt":[{"subitem_source_identifier":"2313433X","subitem_source_identifier_type":"EISSN"}]},"item_10001_version_type_20":{"attribute_name":"出版タイプ","attribute_value_mlt":[{"subitem_version_resource":"http://purl.org/coar/version/c_970fb48d4fbd8a85","subitem_version_type":"VoR"}]},"item_1715043197608":{"attribute_name":"アクセス権","attribute_value_mlt":[{"subitem_access_right":"open 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Extended Power Divergence by Dynamic Parameter Tuning"],"weko_creator_id":"51","weko_shared_id":-1},"updated":"2025-03-19T02:36:40.536678+00:00"}