{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T13:51:28Z","timestamp":1793022688217,"version":"4.5.0"},"reference-count":24,"publisher":"IOP Publishing","issue":"20","license":[{"start":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T00:00:00Z","timestamp":1792972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T00:00:00Z","timestamp":1792972800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Korean Government","award":["NIMS-B26A10000"],"award-info":[{"award-number":["NIMS-B26A10000"]}]},{"name":"National Institute for Mathematical Sciences","id":[{"id":"https:\/\/ror.org\/04n7py080","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Korean Government [Ministry of Science and ICT","award":["RS-2024-00338504"],"award-info":[{"award-number":["RS-2024-00338504"]}],"id":[{"id":"https:\/\/ror.org\/01wpjm123","id-type":"ROR","asserted-by":"crossref"}]},{"name":"National Research Foundation of Korea","id":[{"id":"https:\/\/ror.org\/013aysd81","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,10,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Sparse-view computed tomography (CT) reduces radiation dose and acquisition time but introduces structured streak artifacts that make reconstruction severely ill-posed. Cold-diffusion approaches can model such deterministic degradation processes, but existing sampling strategies may rely on scalar similarity criteria and predefined thresholds to trigger sample-adaptive resets of the degradation-level trajectory. We aim to replace this threshold-based sampling control with spatially resolved residual conditioning.\n                    <jats:italic>Approach.<\/jats:italic>\n                    We propose ReCo-Diff, a residual-conditioned cold-diffusion framework for sparse-view CT reconstruction. At each reverse step, a zero-residual baseline prediction is re-degraded using the CT degradation operator, and the resulting signed image-domain re-degradation discrepancy is used as a spatial conditioning map for a second restoration pass. ReCo-Diff does not require an adaptive reset during residual-conditioned sampling and instead follows a predetermined degradation schedule. A one-time level transition is additionally used for highly sparse inputs. The method was evaluated on the AAPM Low-Dose CT dataset under 18-, 36-, and 72-view settings using a patient-wise split with one held-out test volume.\n                    <jats:italic>Main results.<\/jats:italic>\n                    ReCo-Diff achieved PSNR values of 38.54, 42.57, and 46.98 dB at 18, 36, and 72 views, respectively. At 18 views, matched-computation analysis showed that ReCo-Diff achieved 38.54 dB compared with 38.25 dB for a two-pass zero-residual control under the same 18-NFE budget. A spatially shifted-residual control achieved 37.65 dB, indicating that effective residual conditioning benefits from spatial correspondence with the current degraded state.\n                    <jats:italic>Significance.<\/jats:italic>\n                    ReCo-Diff replaces adaptive trajectory control with step-wise spatial residual conditioning in deterministic cold diffusion. Matched-computation analysis shows that the reconstruction benefit depends on spatially aligned residual information rather than on the additional network evaluation alone. The proposed strategy provides an alternative approach to controlling error propagation under a predetermined degradation schedule without relying on sample-adaptive reset decisions.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/aeb34a","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T22:52:46Z","timestamp":1791845566000},"page":"205027","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ReCo-Diff: residual-conditioned deterministic sampling for cold diffusion in sparse-view CT"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4764-8810","authenticated-orcid":true,"given":"Yong","family":"Eun Choi","sequence":"first","affiliation":[{"name":"National Institute for Mathematical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0032-4630","authenticated-orcid":true,"given":"Hyoung","family":"Suk Park","sequence":"additional","affiliation":[{"name":"National Institute for Mathematical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2460-7478","authenticated-orcid":false,"given":"Kiwan","family":"Jeon","sequence":"additional","affiliation":[{"name":"National Institute for Mathematical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8280-4026","authenticated-orcid":false,"given":"Hyun-Cheol","family":"Park","sequence":"additional","affiliation":[{"name":"Korea National University of Transportation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1141-938X","authenticated-orcid":true,"given":"Sung","family":"Ho Kang","sequence":"additional","affiliation":[{"name":"National Institute for Mathematical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,10,26]]},"reference":[{"key":"pmbaeb34abib1","article-title":"American Association of Physicists in Medicine (AAPM)","year":"2016","unstructured":"American Association of Physicists in Medicine (AAPM) 2016 Low Dose CT Grand Challenge AAPM (available at: https:\/\/www.aapm.org\/GrandChallenge\/LowDoseCT\/ )","type":"dataset"},{"key":"pmbaeb34abib2","first-page":"pp 41259","type":"conference-proceedings","article-title":"Cold diffusion: inverting arbitrary image transforms without noise","volume":"vol 36","author":"Bansal","year":"2023"},{"key":"pmbaeb34abib3","doi-asserted-by":"publisher","first-page":"2277","DOI":"10.1056\/NEJMra072149","type":"journal-article","article-title":"Computed tomography\u2013an increasing source of radiation exposure","volume":"357","author":"Brenner","year":"2007","journal-title":"New Engl. 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Med."},{"key":"pmbaeb34abib4","first-page":"pp 140","type":"conference-proceedings","article-title":"Cross-view generalized diffusion model for sparse-view CT reconstruction","author":"Chen","year":"2025"},{"key":"pmbaeb34abib5","article-title":"Diffusion posterior sampling for general noisy inverse problems","author":"Chung","year":"2023a","type":"conference-proceedings"},{"key":"pmbaeb34abib6","first-page":"pp 7158","type":"conference-proceedings","article-title":"Direct diffusion bridge using data consistency for inverse problems","volume":"vol 36","author":"Chung","year":"2023b"},{"key":"pmbaeb34abib7","first-page":"pp 25683","type":"conference-proceedings","article-title":"Improving diffusion models for inverse problems using manifold constraints","volume":"vol 35","author":"Chung","year":"2022"},{"key":"pmbaeb34abib8","doi-asserted-by":"publisher","first-page":"3586","DOI":"10.1109\/TMI.2024.3475516","type":"journal-article","article-title":"Solving zero-shot sparse-view CT reconstruction with variational score solver","volume":"44","author":"He","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbaeb34abib9","article-title":"Classifier-free diffusion guidance","author":"Ho","year":"2022","type":"conference-proceedings"},{"key":"pmbaeb34abib10","doi-asserted-by":"publisher","first-page":"4509","DOI":"10.1109\/TIP.2017.2713099","type":"journal-article","article-title":"Deep convolutional neural network for inverse problems in imaging","volume":"26","author":"Jin","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"pmbaeb34abib11","doi-asserted-by":"publisher","DOI":"10.1109\/TRPMS.2026.3663178","type":"journal-article","article-title":"Human-guided shading artifact suppression in CBCT-to-MDCT translation via Schr\u00f6dinger bridge with conditional diffusion","author":"Kang","year":"2026","journal-title":"IEEE Trans. Radiat. Plasma Med. Sci."},{"key":"pmbaeb34abib12","article-title":"ADAM: a method for stochastic optimization","author":"Kingma","year":"2015","type":"conference-proceedings"},{"key":"pmbaeb34abib13","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1109\/TRPMS.2018.2867611","type":"journal-article","article-title":"Deep-neural-network-based sinogram synthesis for sparse-view CT image reconstruction","volume":"3","author":"Lee","year":"2019","journal-title":"IEEE Trans. Radiat. Plasma Med. 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Biol."},{"key":"pmbaeb34abib22","article-title":"Score-based generative modeling through stochastic differential equations","author":"Song","year":"2021","type":"conference-proceedings"},{"key":"pmbaeb34abib23","doi-asserted-by":"publisher","first-page":"3002","DOI":"10.1109\/TMI.2021.3078067","type":"journal-article","article-title":"Drone: dual-domain residual-based optimization network for sparse-view CT reconstruction","volume":"40","author":"Wu","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbaeb34abib24","first-page":"pp 108","type":"conference-proceedings","article-title":"Cosign: few-step guidance of consistency model to solve general inverse problems","author":"Zhao","year":"2024"}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb34a","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb34a\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb34a","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb34a\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb34a\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb34a\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb34a\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb34a\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,26]],"date-time":"2026-10-26T13:22:16Z","timestamp":1793020936000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/aeb34a"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,26]]},"references-count":24,"journal-issue":{"issue":"20","published-online":{"date-parts":[[2026,10,26]]},"published-print":{"date-parts":[[2026,10,28]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/aeb34a","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,26]]},"assertion":[{"value":"ReCo-Diff: residual-conditioned deterministic sampling for cold diffusion in sparse-view CT","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2026 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-06-11","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-10-12","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-10-26","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}