{"as_of":"2026-08-08T20:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1c3ed35ef823af6e7019be26450215e86f1e6e052f77a24086f8038e40f79ddc","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T13:43:35.755271Z","state":"measured"},{"denominator":46,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":46,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2509.00395/citation-record","integrity":"/paper/2509.00395/integrity","json":"/paper/2509.00395/citation-record.json","paper":"/paper/2509.00395"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:45.183650Z","title":"Automated brain tumor detection and segmentation for treatment response assessment using amino acid PET,","venue":null,"work_id":"4d459b43-e471-414d-8f60-da4470efecaf","year":2023},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:30.496540Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:e36b0de9c4531ae3216d46e652d5612aadb66ab8040cb5f8a76881f4f70332f5","observation_id":"f218c89b-888a-47b8-8dc2-a933dc722447","resolution":{"observed_at":"2026-08-05T13:43:45.274931Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:44.940951Z","title":"PET imaging of neuroinflammation in neurological disorders,","venue":null,"work_id":"abc7726f-143c-4fbe-9021-c101172fe831","year":2020},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:30.556239Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:6904c8a137b2d22aea74867c159f704c47873fdedf82811cad14e060045b3445","observation_id":"6b563f31-cd7d-43e0-9a16-c4b9aeabd258","resolution":{"observed_at":"2026-08-05T13:43:45.046597Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:44.741552Z","title":"The basic principles of FDG-PET/CT imaging,","venue":null,"work_id":"44ce2e31-9a96-44b4-95c3-b8b53792bbe2","year":2014},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:30.666854Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:0caf34782d6cc6f43bb9a0e4b323d2315789e5b9a9037a2703b854c63ca7707d","observation_id":"3ac55841-a180-4b2c-b8fd-a588c5ca115a","resolution":{"observed_at":"2026-08-05T13:43:44.833816Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:44.514409Z","title":"Petformer netw ork enables ultra-low-dose tota l-body PET imaging without structural prior,","venue":null,"work_id":"8f7eb893-5c22-44c0-8d4c-153c3b637de4","year":2024},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:30.811260Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:3e9753c3c6502abc6d7f440cc1e999ace9ade7a83885fc0738ecb11685889ec3","observation_id":"cbbaf3e8-fb2b-4e1b-b700-9e31ad13659c","resolution":{"observed_at":"2026-08-05T13:43:44.642883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:44.264480Z","title":"Fast anisotropic gauss filtering,","venue":null,"work_id":"68b34b80-2a4f-4fdf-a6a0-64505f30c03c","year":2003},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:30.945648Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:5c68fbd4298b13f0d8c9d2784f143b87039068bad625ff5a2324026e6740c27e","observation_id":"350b06a4-4faa-4542-aafc-25f8484654c7","resolution":{"observed_at":"2026-08-05T13:43:44.363004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:44.055882Z","title":"Low dose PET reconstructio n with total variation regularization,","venue":null,"work_id":"782f32af-c07a-4341-bedb-7d20ee5ff7f2","year":2014},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:31.061678Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:101bdf2d75df62ff4a36a2e4234a4ec87073299c24d919282cd69e6385a4c7be","observation_id":"8bed0505-8737-49da-86f8-1ccdefa57c6f","resolution":{"observed_at":"2026-08-05T13:43:44.170992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:43.786872Z","title":"Low dose PET image reconstruction 10 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. xx, NO. x, 2025 with total variation using alternating direction method,","venue":null,"work_id":"e81b7c3a-8476-436c-835d-8f8599845ea7","year":2025},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:31.201544Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:958e26c1d1fa537bfac8f5ddb5aa379181c802b510179df7629575ed1f95c4fb","observation_id":"c80786ed-d1da-4871-be50-2df7dd53ded1","resolution":{"observed_at":"2026-08-05T13:43:43.934466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:43.507081Z","title":"Non-lo cal means denoising of dynamic PET images,","venue":null,"work_id":"b36ed144-47a4-40f5-abd6-9da809bc18bc","year":2013},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:31.344388Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:11304bd1e89608258160c66d6c8eb7a33a52ea2920709888b1374926cc6fc510","observation_id":"4836bc2a-65f0-4012-8ba4-e642ca776dc9","resolution":{"observed_at":"2026-08-05T13:43:43.672844Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:43.210705Z","title":"Spatially guided nonlocal mean approach f o r denoising of PET images,","venue":null,"work_id":"07128b47-f8aa-419d-8f4a-803b241aae5c","year":2020},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:31.459032Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:b95b1377fbdfae6295fb07da433c7c7b627d24b1f71cc6f68038f63615bbc403","observation_id":"2dc400bc-b730-4360-b851-e024e5d06be1","resolution":{"observed_at":"2026-08-05T13:43:43.377826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:42.952271Z","title":"Image denoi sing with block-matching and 3d filtering,","venue":null,"work_id":"b706264d-33c3-4036-94a0-169917c977ef","year":2006},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:31.596421Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:62d954566461095a060c0d0aae076017ff0d3f3e28b9ed0e99a0989f44a1c8e2","observation_id":"54dc51c2-c0b1-4e3e-b1e9-3ee419726147","resolution":{"observed_at":"2026-08-05T13:43:43.063771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:42.711554Z","title":"Anatomically guided PET image reconstruction using cond itional weakly-supervised multi- task learning integrati ng self-attention,","venue":null,"work_id":"0bb7b18a-18be-4935-89af-a924feee8869","year":2098},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:31.727351Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:5fcf6e293d09e232c39ab3249a03d4a0f36a62c4ad783995c305c04f3e0236fd","observation_id":"ad8e881f-8638-441b-824f-95f8f7ae1135","resolution":{"observed_at":"2026-08-05T13:43:42.805472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:42.505769Z","title":"Deep generalized learning model fo r PET image reconstruction,","venue":null,"work_id":"a3a6c9f0-3062-442c-a6a5-e9596b0dae9c","year":2023},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:31.825088Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:60315f77f563428c77eb7acea905c692241aa5e6f9f8ace6f4f487775b966200","observation_id":"efb27156-d6ff-4daa-bd31-95fc672349b7","resolution":{"observed_at":"2026-08-05T13:43:42.606092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.04119","last_updated":"2017-12-12T04:13:27Z","snapshot_observed_at":"2026-08-05T03:49:01.695618Z","submitted_at":"2017-12-12T04:13:27Z","title":"200x Low-dose PET Reconstruction using Deep Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.04119","snapshot_observed_at":"2026-08-05T13:43:31.927882Z","title":"200x low-dose PET reconstruction using deep learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:31.927882Z"},"links":{"cited_paper":"/paper/1712.04119","citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:ffd0ad5ee52597dee070e60d624aa5bd5c828fa711809fe8f912f7d3d9e46ba0","observation_id":"a43727cc-c818-41d6-bab6-2933e6505350","resolution":{"observed_at":"2026-08-05T13:43:31.927882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:42.311000Z","title":"Multi-stage progressive image restoration,","venue":null,"work_id":"cd1e176a-e2fd-431c-8b46-3920dc092dad","year":2021},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:32.057547Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:ba986cdba52aa80d434f345196ffd95c5720b4edf0e9ab429cafa1e15b9d34ba","observation_id":"9cbf22d5-8f9b-4076-afea-3b5485d12905","resolution":{"observed_at":"2026-08-05T13:43:42.396158Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:42.037117Z","title":"Preliminary deep learning-based low dose whole body PET denoising incorpora ting CT information,","venue":null,"work_id":"743bfd8b-9160-486a-a0f5-570afe7a0313","year":2022},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:32.189367Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:1645fbaa27e9ee98411bea7c0cc69f8993224ce6fa6d470fca15d07737cbf086","observation_id":"d9a459bc-7234-4b37-878b-d523f04ef19e","resolution":{"observed_at":"2026-08-05T13:43:42.166967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:41.828860Z","title":"A total-body ultralow-dose PET r econstruction method via image space shuffle u-net and body sampling,","venue":null,"work_id":"5e81b3db-8ff1-4b9b-9a89-d34eafbc40c7","year":2023},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:32.324601Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:f346c2007e7b4992e93c6d7d8d83323ead1e8fea5b8ee78a5128878d0eaccb9f","observation_id":"aa78f614-6e9e-4322-9ba1-d43787f8a29c","resolution":{"observed_at":"2026-08-05T13:43:41.897489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-05T13:43:32.437992Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:32.437992Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:68a0ead5a8263928cf2b5cc0d6cd692facac1bc17e1a2667157f9458e8835856","observation_id":"f9ec8bf8-5449-42e2-9d92-307b46533380","resolution":{"observed_at":"2026-08-05T13:43:32.437992Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:41.608616Z","title":"Iterative PET image reconstruction using convolutional neural network representation,","venue":null,"work_id":"b8cf0120-e586-43aa-b945-3ba2b718b705","year":2018},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:32.559554Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:5a2f4c6029d89f5fd9e6b27d357221f7a5ae18dbcff6e117fe0f9dbf10a2d6c5","observation_id":"a64d16d6-f464-4ed8-940a-6705c4866689","resolution":{"observed_at":"2026-08-05T13:43:41.694662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:41.396972Z","title":"Ultra-low-dose PET reconstruction using generativ e adversarial network with fe ature matching and task-specific perceptual loss,","venue":null,"work_id":"d12b88d7-b665-4b71-8ad7-62a240469003","year":2019},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:32.693348Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:04a67483faaa6c764a59a1b08e58c5b3f19446cba6a63e7300efd8d0c69b6da4","observation_id":"ec5b5b2b-9616-4db1-aeca-3bd57f6ea005","resolution":{"observed_at":"2026-08-05T13:43:41.505972Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:41.178653Z","title":"Generative adversarial networks,","venue":null,"work_id":"20aa3f14-828b-40f3-8f38-168f331b1024","year":2020},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:32.837486Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:dc54888543aeba9b0ce7309bb92c6cae2dd978414247f352d942280c8423ccb1","observation_id":"39658a9d-15b4-44d8-8fc7-c908327a5273","resolution":{"observed_at":"2026-08-05T13:43:41.259076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:40.931578Z","title":"Wasserstein generativ e ad- versarial networks,","venue":null,"work_id":"a3c978fc-67a3-4291-a22e-421d8dc5bd4f","year":2017},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:32.974009Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:08f87b8de36d848569774579454fb433689d5cd24593d8d6038cd2fb2b16d6b3","observation_id":"4dd4c75f-1b95-4635-9af8-25599da6be52","resolution":{"observed_at":"2026-08-05T13:43:41.078614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:40.630318Z","title":"Improved training of wasserstein gans,","venue":null,"work_id":"19478bb3-576c-4d6f-90ee-be6b6294a6e0","year":2017},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:33.085407Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:ea43784e6281272df5c54c695eac1fc17dd043be75a4c713cf19ebcfd4ced24b","observation_id":"34297747-e167-4679-a0fe-8ea8b64fe2e3","resolution":{"observed_at":"2026-08-05T13:43:40.770552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:40.428115Z","title":"3d multi- modality transformer-gan for high-quality PET reconstruction,","venue":null,"work_id":"0d4ab1d3-064b-455c-abc0-fd69a6b19a31","year":2024},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:33.201510Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:84e15c6cd8f051908afa2f9f16d3e06d6b0f7aaab4567c027c9cceabe7b032f9","observation_id":"0a729b26-df11-477f-abcb-2e5eaea74e40","resolution":{"observed_at":"2026-08-05T13:43:40.518569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:40.178975Z","title":"Prior knowledge-guided triple-domain transformer-gan for direct PET reconstruction from low-count sinograms,","venue":null,"work_id":"55c89c3b-1805-4270-902b-d1c48ad1aa77","year":2024},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:33.299581Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:19bd41a42204f0a24aea8cb70636c125de4865bd545a3908345fa8f108762a79","observation_id":"df7cae5a-6dd6-4e60-8dde-61483739c60c","resolution":{"observed_at":"2026-08-05T13:43:40.322988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:39.939348Z","title":"Diffusion transf ormer model with compact prior for low-dose PET reconstruction,","venue":null,"work_id":"926dd3f8-507f-4a46-97b2-d05a108197cc","year":2024},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:33.382309Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:8001853b9d3a70a928a7da0fe33c80b50fa51a8971fd20c4939e7053f1c0557e","observation_id":"a3bdefa1-b68d-48f1-810e-579a46d6a863","resolution":{"observed_at":"2026-08-05T13:43:40.061811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:39.685525Z","title":"Bidirectiona l condition diffusion probabilistic models for PET image denoising,","venue":null,"work_id":"cd93a457-fdad-436c-88a5-9b373245bfb3","year":2024},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:33.502379Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:931489bc026b35a2dc0fff3208bb9466e0630b004e58d87737e0c42c86bdb958","observation_id":"ba446bfa-1ff4-4cad-a27b-cc12644b87ec","resolution":{"observed_at":"2026-08-05T13:43:39.805489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:39.563039Z","title":"PET imag e denoising based on denoising di ffusion probabilistic model,","venue":null,"work_id":"71527eba-a1c6-47c5-815a-ae3a180d0789","year":2024},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:33.616793Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:426d6f88e36610054f09fd5a5deea1fb63d066fb2bceadeda0d6cdd5d74073cb","observation_id":"f4e76f52-3008-4c2b-824d-40746da1a04f","resolution":{"observed_at":"2026-08-05T13:43:39.625624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:33.698058Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:33.698058Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:8893354790551d9f21596696c0d5594c7d2e3c965701fc7a6740efb7e7972ab7","observation_id":"84117d70-f6a2-4e7d-92e7-3a1b0244eee6","resolution":{"observed_at":"2026-08-05T13:43:33.698058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.02502","last_updated":"2022-10-05T20:19:21Z","snapshot_observed_at":"2026-07-06T10:01:50.133383Z","submitted_at":"2020-10-06T06:15:51Z","title":"Denoising Diffusion Implicit Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.02502","snapshot_observed_at":"2026-08-05T13:43:33.798430Z","title":"Denoising diffusion implicit m odels,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:33.798430Z"},"links":{"cited_paper":"/paper/2010.02502","citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:e46b85e6a285f71447eec13e7b9f7102b08c70af9e63d979a8769b43a7dae1f0","observation_id":"32ffe4cb-8ce7-437a-bdb1-95d9049cc749","resolution":{"observed_at":"2026-08-05T13:43:33.798430Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:39.334499Z","title":"Improved denoising diffusion probabilistic models,","venue":null,"work_id":"dfcbd1b3-b581-4d57-ac21-70a5a2e21118","year":2021},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:33.895374Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:62325eacb6c49d24040ae048f150a238eefd672b7dea7ab98c168739f2e6fc99","observation_id":"542ca9b6-d0d0-4f15-9ba3-9c5774dad621","resolution":{"observed_at":"2026-08-05T13:43:39.476240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:39.081823Z","title":"PET-diffusion: Unsupervised PET enhancement based on the latent diffusion model,","venue":null,"work_id":"652b75f2-4874-43c4-97ff-81aeaf804852","year":2023},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:34.005015Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:64d23228941c1b6b5c4b1f659e2ae1f6168060a0115f7d2b22ea15fad5528d3e","observation_id":"7a64ffbd-febb-4cc3-8c06-9fcf287ead4a","resolution":{"observed_at":"2026-08-05T13:43:39.208107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:38.838891Z","title":"Contrastive diffusion model with auxiliary guidance for coarse-to-fine PET reconstruction,","venue":null,"work_id":"a0b2f520-0acd-482d-9386-844182aea52f","year":2023},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:34.109143Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:05872bcbe9b24965c08dfb4b36a0781ab0b2cd290867d924005dfc19285c25ab","observation_id":"fc31ad58-12c5-462f-9c32-28fa31a4b857","resolution":{"observed_at":"2026-08-05T13:43:38.948002Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.08901","last_updated":"2023-04-15T09:35:59Z","snapshot_observed_at":"2026-07-06T14:19:19.226515Z","submitted_at":"2022-11-16T13:32:13Z","title":"Brain PET Synthesis from MRI Using Joint Probability Distribution of Diffusion Model at Ultrahigh Fields","version":2},"cited_work":{"arxiv_id":"2211.08901","doi":null,"metadata_source":"pith","pith_arxiv_id":"2211.08901","snapshot_observed_at":"2026-08-05T13:43:35.957095Z","title":"Brain PET Synthesis from MRI Using Joint Probability Distribution of Diffusion Model at Ultrahigh Fields","venue":"cs.MM","work_id":"7d89cc6b-e643-4476-a7ce-beb2faa00bf6","year":2022},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:34.205029Z"},"links":{"cited_paper":"/paper/2211.08901","citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:f152150d1eb156f93c2931e7977be1e2f170385b58c445439d1e79bb75aedb95","observation_id":"0f5dd017-a4e2-4dd7-a671-a6369adce513","resolution":{"observed_at":"2026-08-05T13:43:36.063911Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:38.605872Z","title":"Synthesizing PET images from high-field and ultra-high-field MR images using joint diffusion attention model,","venue":null,"work_id":"12801cc1-efa1-4508-8fa9-e2d4df55a3fb","year":2024},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:34.310977Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:11bb3e18b4ada668e8e75541edf9dfce67db76ba10287891560dfec15679c6fb","observation_id":"97743275-dc93-45f1-b70f-0597b680d515","resolution":{"observed_at":"2026-08-05T13:43:38.704881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:38.388872Z","title":"Joint diffusion: mutual consistency-dr iven diffusion model for PET-MR I co- reconstruction,","venue":null,"work_id":"4b016d6e-6728-4cd7-97b7-e6661692977e","year":2024},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:34.444172Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:8141ed497a923aaf73b7c18f84f59f8998108818dbb3ff1826df478a714e32ae","observation_id":"b8f04ad7-e830-4e84-88af-17a2fd3f3c16","resolution":{"observed_at":"2026-08-05T13:43:38.480231Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:38.140288Z","title":"Full- dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model,","venue":null,"work_id":"af002dee-cd8a-4f35-8849-7ab0380086bc","year":2024},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:34.554987Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:d7e641ea4bbfe92ba57e920b3e9be82b08e4e5c0fb696121f243978d1fead50c","observation_id":"1d24acb0-7a70-4b61-9780-404ee48fa32d","resolution":{"observed_at":"2026-08-05T13:43:38.265020Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12996","last_updated":"2025-06-16T23:04:59Z","snapshot_observed_at":"2026-08-05T01:52:21.327248Z","submitted_at":"2024-05-02T20:55:07Z","title":"Dose-aware Diffusion Model for 3D PET Image Denoising: Multi-institutional Validation with Reader Study and Real Low-dose Data","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12996","snapshot_observed_at":"2026-08-05T13:43:34.692701Z","title":"Dose-aware diffusion model for 3d low-dose PET: multi-instituti onal validation with reader study and real low-dose data,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:34.692701Z"},"links":{"cited_paper":"/paper/2405.12996","citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:389d78e91233b78cee1d676f939362a46a8cf475c320c2c499c674e7764e1138","observation_id":"59c50919-59ff-4c29-8146-e42f8a9fae54","resolution":{"observed_at":"2026-08-05T13:43:34.692701Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:37.911365Z","title":"Image super-resolution via iterative refinement,","venue":null,"work_id":"8e5fed62-ee43-428b-88a4-f5ba1d97e967","year":2022},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:34.786051Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:5f30977821d19ce46d7ac0364ecde17bb8ef6228644629d7c304b9bae1c39539","observation_id":"1638009a-4013-4121-96da-e6a9b302d87f","resolution":{"observed_at":"2026-08-05T13:43:38.010015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:37.709943Z","title":"Diffusion models beat GANs on image synthesis,","venue":null,"work_id":"7a114e6d-3ff6-46ed-a990-6a324dc103f2","year":2021},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:34.886347Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:95496a7e3336c7400b0c35458c27c1dfa97b7f36f4102048eec99bcab63a776e","observation_id":"a3137415-e71d-43fe-8066-301121c13d2e","resolution":{"observed_at":"2026-08-05T13:43:37.798512Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:37.442896Z","title":"Ma sked autoencoders are scalable vision learners,","venue":null,"work_id":"10c4d314-d1aa-46ea-8fdd-1a3640489ad8","year":2022},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:34.988086Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:b6fe6278a68aca9d4b219466e1e5b9ce974e15439b50af1dcbbe4f03360a9efa","observation_id":"30e4fdc4-701d-4e23-b6e9-be5dc72f392f","resolution":{"observed_at":"2026-08-05T13:43:37.584489Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:37.149722Z","title":"End-to-end object detection with transformers,","venue":null,"work_id":"5a4544a3-08cb-4657-a084-20e2f514eaea","year":2020},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:35.102057Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:94023bb8fbc09a6a32d14672550c908e731e17bbaa9e5fadde85f74f27f0504c","observation_id":"c62aea81-1c07-4c83-b9fc-9a677ca56d6d","resolution":{"observed_at":"2026-08-05T13:43:37.284086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:36.927745Z","title":"White-box transformers via sparse rate reduction,","venue":null,"work_id":"0193ff5a-6290-46af-b25c-6e355db53d5c","year":2023},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:35.199898Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:c9310af315efbc6c73488ba0577b887f287b3eda1e97fd0ab49b48d26adef8dc","observation_id":"b9a41dcf-7426-4913-8ec2-79e7c744f72f","resolution":{"observed_at":"2026-08-05T13:43:37.041696Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:36.713068Z","title":"U-net: Convolutional networks for biomedical image segmentation,","venue":null,"work_id":"f7d1f6e5-d888-46f3-bb69-4c1e459c5dbb","year":2015},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:35.368208Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:61937e5fc8781e25e2f78b95db5678ff3be1965366e4b7956404e81d8fb40f50","observation_id":"1857a7f7-ec1b-4f27-ab88-bc96f58aed8e","resolution":{"observed_at":"2026-08-05T13:43:36.811549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:36.485725Z","title":"Image-to-image translation with conditional adversarial networks,","venue":null,"work_id":"1e3b38f8-bdf3-4853-9a5d-302ec8b68aec","year":2017},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:35.480518Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:ee8f42f193123e5af85313edefb5c7e189bc09468e463e502ead14a7b172c79f","observation_id":"c4e9f1b7-c20e-4ee1-a4ce-68e8e73f1eae","resolution":{"observed_at":"2026-08-05T13:43:36.604154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T13:43:36.256606Z","title":"Adding conditional control to text- to-image diffusion models,","venue":null,"work_id":"0e61bf34-ff0d-490a-bc10-529347a226bc","year":2023},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:35.615696Z"},"links":{"citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:a6d02db6417bda0549fdb97cb852ad1d58631b4f8cb805e458ce153b93b62e82","observation_id":"bad95925-894a-4f70-a4d2-7f511d7b4567","resolution":{"observed_at":"2026-08-05T13:43:36.366855Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.03636","last_updated":"2024-07-04T05:01:10Z","snapshot_observed_at":"2026-07-06T18:41:18.308899Z","submitted_at":"2024-07-04T05:01:10Z","title":"Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.03636","snapshot_observed_at":"2026-08-05T13:43:35.755271Z","title":"Diff- restorer: Unleashing visual prompts for diffusion-based univers al image restoration,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T13:43:35.755271Z"},"links":{"cited_paper":"/paper/2407.03636","citing_paper":"/paper/2509.00395"},"observation_digest":"sha256:0c19e25c9eb430581f0dfb9827957d5ecc4249e1804138115b12ec55a3cf6b44","observation_id":"afacd7c6-1bf7-44eb-a034-0ba79d5d9023","resolution":{"observed_at":"2026-08-05T13:43:35.755271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.00395","last_updated":"2025-08-30T07:22:26Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-07T08:11:21.190043Z","submitted_at":"2025-08-30T07:22:26Z","title":"Double-Constraint Diffusion Model with Nuclear Regularization for Ultra-low-dose PET Reconstruction"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":1,"verified_fuzzy":39},"total_outbound_references":46},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2509.00395."}