{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T12:30:33Z","timestamp":1748953833986,"version":"3.37.3"},"reference-count":27,"publisher":"IOP Publishing","issue":"21","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Key Clinical Specialty Project of Shanghai"},{"name":"National Key Research and Development Program of China","award":["2022YFC2404603"],"award-info":[{"award-number":["2022YFC2404603"]}]},{"name":"The Shanghai Committee of Science and Technology Fund","award":["21Y21900200"],"award-info":[{"award-number":["21Y21900200"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["12375339"],"award-info":[{"award-number":["12375339"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Xuhui District Artificial Intelligence Medical Hospital Cooperation Project"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2024,11,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>\n                  <jats:italic>Objective<\/jats:italic>. The study aims to reduce the imaging radiation dose in Adaptive Radiotherapy (ART) while maintaining high-quality CT images, critical for effective treatment planning and monitoring. <jats:italic>Approach<\/jats:italic>. We developed the Prior-aware Learned Primal-Dual Network (pLPD-UNet), which uses prior CT images to enhance reconstructions from low-dose scans. The network was separately trained on thorax and abdomen datasets to accommodate the unique imaging requirements of each anatomical region. <jats:italic>Main results<\/jats:italic>. The pLPD-UNet demonstrated improved reconstruction accuracy and robustness in handling sparse data compared to traditional methods. It effectively maintained image quality essential for precise organ delineation and dose calculation, while achieving a significant reduction in radiation exposure. <jats:italic>Significance<\/jats:italic>. This method offers a significant advancement in the practice of ART by integrating prior imaging data, potentially setting a new standard for balancing radiation safety with the need for high-resolution imaging in cancer treatment planning.<\/jats:p>","DOI":"10.1088\/1361-6560\/ad7b9b","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T12:44:29Z","timestamp":1726749869000},"page":"215004","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Prior-image-based low-dose CT reconstruction for adaptive radiation therapy"],"prefix":"10.1088","volume":"69","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7757-0966","authenticated-orcid":true,"given":"Yao","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9012-6605","authenticated-orcid":true,"given":"Jiazhou","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4409-6804","authenticated-orcid":true,"given":"Weigang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"article-title":"Odlgroup\/odl: ODL 0.7.0","year":"2018","author":"Adler","key":"pmbad7b9bbib1"},{"key":"pmbad7b9bbib2","doi-asserted-by":"publisher","first-page":"1322","DOI":"10.1109\/TMI.2018.2799231","article-title":"Learned primal-dual reconstruction","volume":"37","author":"Adler","year":"2018","journal-title":"IEEE Trans. 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All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2024-04-06","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-09-16","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2024-10-16","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}