{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T16:47:15Z","timestamp":1791046035885,"version":"4.1.0"},"reference-count":48,"publisher":"IOP Publishing","issue":"14","license":[{"start":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:00:00Z","timestamp":1752624000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/page\/copyright"},{"start":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:00:00Z","timestamp":1752624000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2022YFE0131100"],"award-info":[{"award-number":["2022YFE0131100"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["12327809"],"award-info":[{"award-number":["12327809"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2025,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Multi-source stationary computed tomography (MSS-CT) offers significant advantages in medical and industrial applications due to its gantryless scan architecture and capability of simultaneous multi-source emission. However, the lack of anti-scatter grid deployment in MSS-CT leads to severe forward and cross scatter contamination, necessitating accurate and efficient scatter correction. In this work, we propose ComptoNet, an innovative decoupled deep learning framework that integrates Compton-scattering physics with deep learning for scatter estimation in MSS-CT. The core innovation lies in the Compton-map, a representation of large-angle Compton scatter signals outside the scan field of view. ComptoNet employs a dual-network architecture: a conditional encoder\u2013decoder network guided by reference Compton-maps and spare detector data for cross scatter estimation, and a frequency U-Net with attention mechanisms for forward scatter correction. Experiments on Monte Carlo-simulated data demonstrate ComptoNet\u2019s superior performance, achieving a mean absolute percentage error of 0.84% on scatter estimation. After correction, CT images show nearly artifact-free quality for all test phantoms, validating ComptoNet\u2019s robustness in mitigating scatter-induced errors across diverse photon counts and phantoms comparing with other methods.<\/jats:p>","DOI":"10.1088\/1361-6560\/adebd7","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T18:54:53Z","timestamp":1751568893000},"page":"145022","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["ComptoNet: a Compton-map guided deep learning framework for multi-scatter estimation in multi-source stationary CT"],"prefix":"10.1088","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-2094-4576","authenticated-orcid":true,"given":"Yingxian","family":"Xia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3633-9578","authenticated-orcid":true,"given":"Li","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9723-5655","authenticated-orcid":true,"given":"Yuxiang","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1626-4401","authenticated-orcid":true,"given":"Hewei","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2025,7,16]]},"reference":[{"key":"pmbadebd7bib1","doi-asserted-by":"publisher","first-page":"4878","DOI":"10.1118\/1.3231824","article-title":"Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit","volume":"36","author":"Badal","year":"2009","journal-title":"Med. 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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":"2025-04-27","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-07-03","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2025-07-16","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}