{"as_of":"2026-08-07T15:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:33955c915079d3178a9f5edc295fcbcdb3cf289fe76d3e2f4fcc898257dcba55","coverage":[{"denominator":59,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":59,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-10T15:42:05.131941Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2604.12152/citation-record","integrity":"/paper/2604.12152/integrity","json":"/paper/2604.12152/citation-record.json","paper":"/paper/2604.12152"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/nbm.4992","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"NMR in Biomedicine","work_id":"00891e77-2a8a-4494-b7b1-d66b4cf496ad","year":2024},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:3c6bff3292cf93e0812135f2e6ccdb8970b41e90ed645f83f997d25ed64e8119","observation_id":"b942e952-eec1-4c57-bed8-05986d2fe512","resolution":{"observed_at":"2026-05-10T15:45:33.858963Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/jmri.28408","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Low-field MRI: Clinical promise and challenges.Journal of Magnetic Resonance Imaging, 57(1): 25–44","venue":"Journal of Magnetic Resonance Imaging","work_id":"26043ee8-5ed2-4544-bb65-ff4ca8f7bdd4","year":2023},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:278f71f0448f625797a7f3ad75405a617a1639556d57ecf5e2a1a841f170d874","observation_id":"a4031129-0770-4b18-b0ca-89012252a429","resolution":{"observed_at":"2026-05-10T15:45:33.865289Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/nbm.4846","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A framework for advancing sustainable magnetic resonance imaging access in Africa.NMR in Biomedicine, 36(3):e4846","venue":"NMR in Biomedicine","work_id":"c1734cf5-c6bf-408b-ba69-fd23eef58598","year":2023},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:5755fce9224bd80ad8ccd5684b1081b1fd8eecc0a85374446568b531f9ea48c0","observation_id":"1447c6a1-3ff7-46aa-b534-067afaef8b9b","resolution":{"observed_at":"2026-05-10T15:45:33.862214Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Low field, high impact: Democratizing MRI for clinical and research innovation.BJR |Open, 7(1):tzaf022","venue":null,"work_id":"12e6eb0f-2be0-41db-ac06-4e7ccbf20aa8","year":2025},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:cc00bb501523166731c60312ba9b526b612691aeef92896dab49ca9d728e72e2","observation_id":"9b998744-97e2-45a0-b7f5-1eb63dd94439","resolution":{"observed_at":"2026-05-17T19:10:07.984227Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Temporal and spatial super resolution with latent diffusion model in medical MRI images","venue":null,"work_id":"eec57978-41b3-4071-9df4-30fbe4205444","year":2024},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:9dcadd56f70c49ae3d7a85a4d08c2065f825ef5a34a3f052317a76538b9afeea","observation_id":"c298044f-d3b0-4575-9288-2640ce962cea","resolution":{"observed_at":"2026-05-17T19:10:07.990652Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-031-43999-5","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"InverseSR: 3D brain MRI super-resolution using a latent diffusion model","venue":"Lecture notes in computer science","work_id":"2d62c60c-98fc-4f95-941a-6dcf6e61f95f","year":2023},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:e4278fd114546432a7c7e3430db396bc56980b3c6e1a350cbb3aa7687a930e8b","observation_id":"981c5331-d96b-4525-83ae-f5508f56d5bf","resolution":{"observed_at":"2026-05-10T15:45:33.852545Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"sFRC for assessing hallucinations in medical image restoration","venue":null,"work_id":"a3f6af9b-e398-4e92-a1fe-261f865affb4","year":2026},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:d98cacfa16d78f74260b03a0c250f03dd4c5809564ce98c89c6a80fe0e93b7d8","observation_id":"e060be5a-d635-4bd8-a414-981de62237ab","resolution":{"observed_at":"2026-05-17T19:10:07.987454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Hallucination score: Towards mitigating hallucinations in generative image super-resolution","venue":null,"work_id":"13322fcf-06ad-45d3-92de-abfeb71627df","year":2025},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:8e3080d38e5842fd6a40740b1e5746e785c8870bb1f5d80b1a3725f259c2a1ed","observation_id":"f98c56aa-1640-4db8-b00f-d6e9feea54de","resolution":{"observed_at":"2026-05-17T19:10:07.981113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Jones, Jonathan Lee, and Meng Law","venue":null,"work_id":"a121c8e0-89d4-4efa-a888-262bf4c7c577","year":null},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:6c3262e2efbcc2b294232e50b8a476a1f322ee091d82b60f4c83aac91b515684","observation_id":"5bba14f3-3980-43c4-acb4-53e7d512daef","resolution":{"observed_at":"2026-05-17T19:10:07.977849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.mric.2020.09.001","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi: 10.1016/j.mric.2020.09.001","venue":"Magnetic Resonance Imaging Clinics of North America","work_id":"d7b6b010-2bd6-4f4e-a1bd-d30ef2898d11","year":2020},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:b7fae8fefc27210395b2d365b1cb49f2ee45f7b8101b6669f608e7a641e7e179","observation_id":"9b56d6db-be20-4501-8fbb-17bd5044b8b0","resolution":{"observed_at":"2026-05-10T15:45:33.841038Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.pnmrs.2018.06.001","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ladd, Peter Bachert, Martin Meyerspeer, Ewald Moser, Armin M","venue":"Progress in Nuclear Magnetic Resonance Spectroscopy","work_id":"c2da8267-1747-414e-ad81-c581ef0a697c","year":2018},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:ab8d5f774cf6b1255574fa9954dfda2f93e02b85646f5047cbe3052fb481d7fa","observation_id":"f114eae9-c379-47ad-9c8b-cffae20689be","resolution":{"observed_at":"2026-05-10T15:45:33.844401Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2020.298216","doi":"10.1109/tpami.2020.2982166","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","work_id":"88eedb71-11b4-44a5-af82-1c773de8a011","year":2020},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:3d495125ad5d3ddd6c8b78272eb5131370a68ef00f5a5148ebfba70979a00ab3","observation_id":"d5f88576-ef1d-44c4-bb6c-0137986ec978","resolution":{"observed_at":"2026-05-10T15:45:33.848769Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-25T18:53:57.468599+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-25T18:53:57.468599+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.sigpro.2016.05","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Image super-resolution: The techniques, applications, and future.Signal Processing, 128:389–408, November 2016","venue":null,"work_id":"4913db0c-9f64-46da-92b5-0879144222e8","year":2016},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:0c4e986ad3c6c701a3b98f0233820a569f9d37478fc064be714547ec5201de91","observation_id":"7471c581-d6c5-47b4-99af-3c29186adbfe","resolution":{"observed_at":"2026-05-10T15:45:33.798501Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1145/3390462","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"A deep journey into super-resolution: A survey.ACM Computing Surveys, 53(3):60:1–60:34","venue":"ACM Computing Surveys","work_id":"d819e771-7e1d-4446-a304-0c4ec8d8dea8","year":2021},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:58363b8ace3ae18424596010d367347f9ac5dc6c4a1646eb0c73dc7cc8c3defd","observation_id":"57b686a1-334e-41c4-845f-e5799049c0f3","resolution":{"observed_at":"2026-05-10T15:45:33.775634Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/jmri.1880040517","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Du, Dennis L","venue":"Journal of Magnetic Resonance Imaging","work_id":"502a9bb1-adbb-41ae-a81f-3fab7623f1d7","year":1994},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:41b89f28ee59a46dab39d465dc4ead49c80de11d177521785c4b50b2325243e4","observation_id":"b0bcde80-32b8-4a9c-810b-2bc5e529f2ac","resolution":{"observed_at":"2026-05-10T15:45:33.801278Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/s0730-725x(02)00511-8","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Greenspan, G","venue":"Magnetic Resonance Imaging","work_id":"25600321-ca97-4e8d-bda7-da2659e9c77e","year":2002},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:e607f36713d39ba1248102fc7234aaa11a9d3634affe976730575ab43736264e","observation_id":"882af65e-01b2-4922-85b6-02012a3790a1","resolution":{"observed_at":"2026-05-10T15:45:33.764513Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1117/1.jmi.1.3.034007","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Journal of Medical Imaging","work_id":"1482e94f-4e1c-4123-8cf3-6b6cb910352b","year":2014},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:3c9632468be269a36c4a166de1d368b40f6b1336720003d702a55dcd9147e22b","observation_id":"2c17dde7-3742-413c-9f5b-cb9c47aea88f","resolution":{"observed_at":"2026-05-10T15:45:33.758796Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2019.101647","doi":"10.1016/j.compmedimag.2019.101647","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Multiscale brain MRI super-resolution using deep 3D convolutional networks.Computerized Medical Imaging and Graphics, 77:101647, October 2019","venue":"Computerized Medical Imaging and Graphics","work_id":"cda67e51-42d7-415b-9fea-430300612457","year":2019},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:9abdeb4479081d916304869b0ba027afb9e327630be89e3247fe34cbd736ad64","observation_id":"8c7af89d-80fb-48be-ae07-4becd372de24","resolution":{"observed_at":"2026-05-10T15:45:33.813380Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2018.836367","doi":"10.1109/isbi.2018.8363675","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Real-time automatic fetal brain extraction in fetal MRI by deep learning","venue":null,"work_id":"a9e330c2-e9b3-4b8d-a182-a9844dc1c491","year":2018},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:f09c24b3437c2be8dfa087303e991fc25df029c8c0bff7af981a228925557e5e","observation_id":"f82b0a9e-5ee9-48b8-a937-08dcab9d13b1","resolution":{"observed_at":"2026-05-10T15:45:33.817474Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Masutani, Naeim Bahrami, and Albert Hsiao","venue":null,"work_id":"d6b4ebc5-684d-46be-87d7-4bb7d4833f2d","year":null},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:d67aef5545554b5a791c0d4151565f35b109384c13ff4c68992756f4e173341a","observation_id":"4f9f6459-33f3-4d64-b587-c22684812c77","resolution":{"observed_at":"2026-05-17T19:10:08.023370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1148/radiol.2020192173","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi: 10.1148/radiol.2020192173","venue":"Radiology","work_id":"9f4ee4db-6a9f-4417-b494-bc1ac23ebc8f","year":null},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:ac667c52248208219da5de90570be97d51b615bacbce776735afae1faef02518","observation_id":"944540c5-2812-4330-a6dd-48ee35339e81","resolution":{"observed_at":"2026-05-10T15:45:33.822445Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-030-00928-1","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Christodoulou, Yibin Xie, Zhengwei Zhou, and Debiao Li","venue":"Lecture notes in computer science","work_id":"7e90442c-cce3-4c5f-aacd-ae84f713cf96","year":2018},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:4258d579757b7913cbcb1e463c131f76caf0f44c09cfe6b14a10a85244c0c50c","observation_id":"9738c439-84a5-4107-b9a5-f533c4d8bd38","resolution":{"observed_at":"2026-05-10T15:45:33.820007Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-14T19:49:59.605233+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T19:49:59.605233+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks, September 2018","venue":null,"work_id":"2f34d8b0-d9d1-472a-a085-353cb70a68a6","year":2018},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:55c91b770762a81b1f9f75dc5acad708d8712ce8250336814442ac3c1e5e0a3d","observation_id":"ddb05eef-405f-4ef8-a569-52cdda161922","resolution":{"observed_at":"2026-05-17T19:10:08.013638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41598-022-10298-6","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Scientific Reports","work_id":"8e1a681f-7f18-428c-a360-cd1c34a2aa7d","year":2022},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:8520f223a9ad2858ebf1865b6850757cfb6f255840968015985907efcac4b35c","observation_id":"708f123b-8ea5-4582-b54a-dc7ca70be154","resolution":{"observed_at":"2026-05-10T15:45:33.803797Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/hbm","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Martin, Rhodri Cusack, and Stefan Köhler","venue":null,"work_id":"761184f0-ff25-4b32-9e40-b17ffaf321df","year":2018},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:67240a158a2d743a776d293655f50024735707458276b9559d0db3abf967f9ed","observation_id":"3cbf24c3-1add-440d-9755-294586fb0a20","resolution":{"observed_at":"2026-05-10T15:45:33.795325Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/mrm.27178","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Kiki- net: Cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images","venue":"Magnetic Resonance in Medicine","work_id":"d4801fa8-e377-435a-ace0-69f5bf6b2376","year":2018},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:17bb85acb91c378d2cfb3b1db4eb9884e99cc0d0669ddd6b9ebc1058a4ce836c","observation_id":"f23d66dd-9b1e-4bc0-af44-6e2584e072c0","resolution":{"observed_at":"2026-05-10T15:45:33.806558Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10334-023-01123-w","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Magnetic Resonance Materials in Physics Biology and Medicine","work_id":"3067881d-5979-4db5-a2e6-bcd122430b0f","year":2024},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:b943ab6e97337a623443d3335e51e37de64a5bc9db4b38800744befe7a510f3d","observation_id":"65fb74f6-3a5f-41d8-91cc-6dcecfc51723","resolution":{"observed_at":"2026-05-10T15:45:33.785483Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":"276761be-6955-42c1-8b9f-38811eb86f54","year":2022},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:2aaf12531bdb88cd7a56e641fff3368af7c10cafafd1f1f4daa504f51f85d4e9","observation_id":"9a0425d4-75bf-4b07-ad14-e810c7eb2b46","resolution":{"observed_at":"2026-05-17T19:10:08.017071Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.107590","doi":"10.1016/j.cmpb.2023.107590","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Medical image super-resolution reconstruc- tion algorithms based on deep learning: A survey.Comput","venue":"Computer Methods and Programs in Biomedicine","work_id":"9ed8d95b-cf4d-42c9-8ee7-0d7fa605f00a","year":2023},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:c69003f87c389ef1511e38092e4675aeac38dca5196560d5a1c7247808122f58","observation_id":"80dc6c0b-5785-4b59-bae4-6f6321d852da","resolution":{"observed_at":"2026-05-10T15:45:33.792674Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/mrm.24187","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Magnetic Resonance in Medicine","work_id":"8a5d4c3c-78eb-4898-bef4-7bdbabdedb90","year":1983},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:e3e1c6fc8924c17864c0c994a987e1c82b7174cd76c8946f4fb58b1cbf29c3ff","observation_id":"f9656f08-209d-4349-9bb7-397ab757a20d","resolution":{"observed_at":"2026-05-10T15:45:33.782262Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-031-18576-2","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"Lecture notes in computer science","work_id":"bf59c28e-6689-4a14-b78a-99355b4d9b3c","year":2022},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:1a53c4038e19ecb1a9b569c319146716d30e9317eb27ed28606a6385f7ee4509","observation_id":"a7ad3bd6-8c4d-4388-afc6-8bad09d04bae","resolution":{"observed_at":"2026-05-10T15:45:33.761500Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.102479","doi":"10.1016/j.media.2022.102479","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"doi: https://doi.org/10.1016/j.media.2022.102479","venue":"Medical Image Analysis","work_id":"28a6e047-9d28-4467-bcb5-14b6111a2236","year":2022},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:9d9ca6dbfbbb421c1a3ffaf7a34bde2563e1f66ac93262020a5e2619eecb8285","observation_id":"e023e884-049d-4437-bbc3-cc3076b848bd","resolution":{"observed_at":"2026-05-10T15:45:33.772565Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Solving inverse problems in medical imaging with score-based generative models","venue":null,"work_id":"441b118e-90bd-4cc0-bbc6-ccaf61fcc847","year":2022},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:701346159e0d6a754fd2ad241f62883fd57060c3f0ef0fb44b5b673ea7b89d8e","observation_id":"55d8f19b-c38a-4653-a8e5-47b9a631c119","resolution":{"observed_at":"2026-05-17T19:10:08.020267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.329014","doi":"10.1109/tmi.2023.3290149","metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"¨Ozbey, O","venue":"IEEE Transactions on Medical Imaging","work_id":"d25d98de-a3a5-43cb-b3e7-e651454f368a","year":2023},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:e5c3a4f07cc1433e2c90d48c0987afa66eb8b2470a6f76aff1c93fe18954476e","observation_id":"90963393-1bd7-4958-97dd-5485ff613ed2","resolution":{"observed_at":"2026-05-10T15:45:33.768636Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"SwinIR: Image restoration using Swin transformer","venue":null,"work_id":"99ad85b6-a4ac-43e6-aac4-6a0df128f6fd","year":2021},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:be7b132366a99f359e6bf9b64ee3fa37d7f1eb70212e1326a13d6eb0b2e32489","observation_id":"e282571d-b121-496e-b77f-086ca65a73b0","resolution":{"observed_at":"2026-05-17T19:10:08.026563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":"52192814-81e9-4172-9425-31bb7eb1acad","year":2020},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:78719d92dad37c7ca9a841117e3a2ed6b51c9c0ec3b43881da419fb8705f2ccb","observation_id":"bb08a2a8-b298-4921-8d26-7300fdc6160b","resolution":{"observed_at":"2026-05-17T19:10:08.035906Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-07-07T15:53:55.094407Z","title":"Denoising diffusion implicit models","venue":null,"work_id":"f72d3d75-110f-4011-8aaf-36042bb11699","year":2021},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:d2080cf712b1acc901ad7821175f4595f1150b54c35343362ad00910aa8e3b16","observation_id":"c954ff92-c79d-497d-a1ce-9fab8c0ea80e","resolution":{"observed_at":"2026-05-17T19:10:08.045739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Inversion by direct iteration: An alternative to denoising diffusion for image restoration.Transactions on Machine Learning Research (TMLR)","venue":null,"work_id":"392362c1-67bb-4bb9-a529-a73a1cab9e9e","year":2023},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:c5eae784f46faa2c992ca2884e9558582e84e729d9f16f443d6abcedd66f2473","observation_id":"6406824a-eec1-4ded-9535-b2d8b9d0840b","resolution":{"observed_at":"2026-05-17T19:10:08.010273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Kleinberg, and Samy Bengio","venue":null,"work_id":"4c84d7fa-3d68-4030-ade8-1ed5133a8950","year":2019},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:f9f782877f50c2e5a73dd235c656e20e99d6f6b28504758dd1fdf27c53a3650c","observation_id":"64bca4f2-f57d-4755-9f15-99cd1b92e380","resolution":{"observed_at":"2026-05-17T19:10:08.029389Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"MedVAE: Efficient Automated Interpretation of Medical Images with Large-Scale Generalizable Autoencoders, June 2025","venue":null,"work_id":"ee0adc11-b6af-4d40-9d87-a69297d92db6","year":2025},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:8ced76ccaf368d1e651f25f19bb2af47e0e032868cee1d528beaad7691751f81","observation_id":"dea7ed71-35be-41aa-a163-e026e9ffb608","resolution":{"observed_at":"2026-05-17T19:10:08.007088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-11T01:07:42.960769Z","title":"Masset, R","venue":null,"work_id":"238df2e4-a3e5-46f3-860e-3ae2b0094b97","year":2019},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:184669c9fff93b85cc9eda4da28b0e909d04249f4ee2faeee920e6bea96ef9ca","observation_id":"ed9e6af4-2ac8-4d60-be10-59e538cdee83","resolution":{"observed_at":"2026-05-10T15:45:33.779027Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anton Schwaighofer, Maria Teodora Wetscherek, Anja Thieme, Matthew P","venue":null,"work_id":"0a025003-8ea1-4529-bff1-6b1e5c12c059","year":2023},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:fc85ef011b096bee78574493e5312f1717f410ffea9e8b10981e03ca4ce0fea7","observation_id":"988b5833-d328-4545-91a3-063c96e251bc","resolution":{"observed_at":"2026-05-17T19:10:08.003873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.10815","last_updated":"2025-02-07T12:03:23Z","snapshot_observed_at":"2026-08-05T01:30:51.584747Z","submitted_at":"2024-01-19T17:02:17Z","title":"Exploring scalable medical image encoders beyond text supervision","version":3},"cited_work":{"arxiv_id":"2401.10815","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2401.10815","snapshot_observed_at":"2026-07-02T21:37:25.259732Z","title":"C.; Schwaighofer, A.; Lungren, M","venue":null,"work_id":"38aa977c-e7a9-4409-a2e2-8bae29a08bd7","year":2024},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"cited_paper":"/paper/2401.10815","citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:5efd86a03991aae781d818cae5b6ac35a760eddc21b56e9f24204b3a4bcb352f","observation_id":"09aa6aa9-3e3e-4e8a-9b5f-a6b62d313e02","resolution":{"observed_at":"2026-05-11T10:01:01.560745Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2022.32044","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Fleet, and Mohammad Norouzi","venue":null,"work_id":"115381ac-b26c-4a59-afb1-21e65dbb64e9","year":2023},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:e4b033b172c3b5290dacc1bfefefd12ba393a2cc7ebd0bce30f983250656e856","observation_id":"d23d535f-67bb-4ae2-bedc-d0afb0346fa7","resolution":{"observed_at":"2026-05-11T10:01:01.546627Z","resolver_source":"arxiv_id","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"DiffIR: Efficient diffusion model for image restoration","venue":null,"work_id":"9515a743-8a06-4de2-8a97-2cd66bb4392d","year":2023},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:995a2ec64e64456479c88ceddbcbfb56b83cf17d08c68d34b61311055f4a77eb","observation_id":"527683a0-455f-4d33-af14-347183078887","resolution":{"observed_at":"2026-05-17T19:10:08.042798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"The perception-distortion tradeoff","venue":null,"work_id":"e64d5df6-5e7f-442b-974e-4becbc8a9ca2","year":2018},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:fbd859112de6a1fbdfb6b3ce114cd481d5571863968748f280ac98d7a91588c5","observation_id":"840d0197-2ff0-4d61-aadb-2a47dda0402c","resolution":{"observed_at":"2026-05-17T19:10:07.997286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Generative modelling with inverse heat dissipation","venue":null,"work_id":"811d9c72-dace-47fb-97db-c78743d123d5","year":2023},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:dece14578843c105b3f15c41a12ae6c7491feb1e94677171134a28932757de69","observation_id":"336a03e2-629b-4282-9855-4ab2a5b06386","resolution":{"observed_at":"2026-05-17T19:10:08.032246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Flow straight and fast: Learning to generate and transfer data with rectified flow","venue":null,"work_id":"e11482ac-ad7e-4c34-bcfb-f78713a86f66","year":2023},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:790266d0fa51360312133ad405a5521037db90ee28657fef52135289ca2eb60d","observation_id":"b9ded7cc-3b30-4be3-a404-2cdb7b06c13c","resolution":{"observed_at":"2026-05-17T19:10:08.039699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1126/science.aax2342","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Science , author =","venue":"Science","work_id":"dd1df3a9-2261-4ba5-b5a3-062dc9f15ecc","year":2019},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:6323fbf7ae79f89bdf70c1a15c7e7e0fa1214f54b9b3324c45134a1796a3397d","observation_id":"7406ed32-5152-4dc7-98d1-0f0cce40f814","resolution":{"observed_at":"2026-05-10T15:45:33.831499Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-11T14:19:30.043431+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T14:19:30.043431+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2967/jnumed.123.266080","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"McCradden, Kathleen Creel, Ronald Boellaard, Eliza- beth C","venue":"Journal of Nuclear Medicine","work_id":"9a44893e-4077-4e2a-aa4f-1f15f581caa6","year":2023},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:4c0a596873464adef4ef1469dfdbee4a3603b04c9ce31a3f0708b40e4172faf8","observation_id":"70aeb350-5fca-46ad-933c-11583e83f52a","resolution":{"observed_at":"2026-05-10T15:45:33.837841Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-031-72787-0","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Practical and Ethical Considerations for Generative AI in Medical Imaging","venue":"Lecture notes in computer science","work_id":"2b76a908-e040-4c70-a008-2c56a1a9fdcd","year":2024},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:75500adaab10c08b8af9aa08ee8899e64b927ce79cc366aa0bdfd1df77e1941e","observation_id":"0c03a48b-d31f-44a2-9e26-e6b7bb80dfe9","resolution":{"observed_at":"2026-05-10T15:45:33.855945Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-07-14T19:50:01.99458+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-14T19:50:01.99458+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1371/journal.pmed.1002699","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Ball, Jeremy Irvin, Allison Park, Erik Jones, Michael Bereket, Bhavik N","venue":"PLoS Medicine","work_id":"06770a44-940a-4f80-ae12-6dc2ce9f9934","year":2018},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:aef20b2445e59c35ebaf0c55be63eef6908cfb49958b44876ae0b3c61a05fd16","observation_id":"f6bfdad8-f503-46c0-b639-ae0c1a5fd146","resolution":{"observed_at":"2026-05-10T15:45:33.834680Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.02314","last_updated":"2021-09-12T20:26:52Z","snapshot_observed_at":"2026-08-06T01:56:28.054412Z","submitted_at":"2021-07-05T23:12:06Z","title":"The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification","version":2},"cited_work":{"arxiv_id":"2107.02314","doi":"10.1109/cvprw63382.2024.00408","metadata_source":"pith","pith_arxiv_id":"2107.02314","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification","venue":"cs.CV","work_id":"647499e6-42d2-4534-9ae2-0f3cf920c652","year":2021},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"cited_paper":"/paper/2107.02314","citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:19a42b9744888dc47559d613311c446c0c0e86010e0336b3434ecc8b7310e0fa","observation_id":"04cc587c-f221-44ca-9f5c-5ea753039c83","resolution":{"observed_at":"2026-05-14T21:29:58.212702Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.07042","last_updated":"2019-11-14T17:34:51Z","snapshot_observed_at":"2026-08-02T04:11:08.699777Z","submitted_at":"2019-01-21T19:01:00Z","title":"MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs","version":5},"cited_work":{"arxiv_id":"1901.07042","doi":null,"metadata_source":"pith","pith_arxiv_id":"1901.07042","snapshot_observed_at":"2026-07-09T23:26:36.938370Z","title":"MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs","venue":"cs.CV","work_id":"9f686305-3144-4f19-b5a5-0dcce109551c","year":2019},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"cited_paper":"/paper/1901.07042","citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:d8825d55736c338b4458379b1e62cb0c0d4ea3a46ba396f81533bb794d8cda27","observation_id":"f16bed2c-85ae-4a7a-bd40-e6da978bf48a","resolution":{"observed_at":"2026-05-17T04:16:17.195024Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.21014/actaimeko.v13i1.1679","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Arabboev, S","venue":"ACTA IMEKO","work_id":"e749060d-99ff-4f69-9557-8accacbdb9bb","year":2024},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:9f3e1e9a7188daab3b04e9581b31ccd71f6f31f6a29e7b3be9d2f1213fdfa4e0","observation_id":"4e1a257c-1fb1-4a3e-a56e-33e9c0c6c946","resolution":{"observed_at":"2026-05-10T15:45:33.828063Z","resolver_source":"doi","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-05-23T09:23:38.064418+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-23T09:23:38.064418+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41598-025-87358-0","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Klemens, Ivo M","venue":"Scientific Reports","work_id":"9008dd95-f62d-4916-9000-012e925ec2f6","year":2025},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:2afa6c93841101927ac93321819b77534a254bcc016f7f90c8741627bcfc90d2","observation_id":"72ffd5f8-6221-441e-be0b-44ec4c5a21a9","resolution":{"observed_at":"2026-05-10T15:45:33.825260Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"Efros, Eli Shechtman, and Oliver Wang","venue":null,"work_id":"89ccc141-f2ac-43c1-bcc7-79607bcd0975","year":2018},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:2bf987d860efbb9f6053aa7af153e0557d246cc94a02d15358babe20c0dd3fe9","observation_id":"7a708d76-fb37-4b02-83e5-b218852315fb","resolution":{"observed_at":"2026-05-17T19:10:07.994091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-06-05T21:23:00.469572Z","title":"GANs trained by a two time-scale update rule converge to a local Nash equi- librium","venue":null,"work_id":"f65438f7-ba24-4a64-b1d8-199d28c528c8","year":2017},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:af50eb630aa4f4958a5718f92330da8aa129fa3f352a73e3927eac2b99569bbd","observation_id":"f5d57ad5-4e70-4956-af20-375831987421","resolution":{"observed_at":"2026-05-17T19:10:08.000896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"3550.1352","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Progressive Distillation for Fast Sampling of Diffusion Models, June 2022","venue":null,"work_id":"61283ff5-4694-4485-a93c-64d7740f7178","year":2022},"citing_paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-10T15:42:05.131941Z"},"links":{"citing_paper":"/paper/2604.12152"},"observation_digest":"sha256:b770cb04ddaac6c78824b56d2c964c947630c0b7454a734f7e4c6ac22be9b099","observation_id":"c4fc4eb5-a6ed-46b7-baf9-f10caebbbe82","resolution":{"observed_at":"2026-05-11T10:01:01.524093Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2604.12152","last_updated":"2026-04-14T00:11:23Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-04T00:48:40.349628Z","submitted_at":"2026-04-14T00:11:23Z","title":"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution"},"reference_resolution":{"displayed":59,"state_counts":{"malformed_identifier":4,"metadata_mismatch":4,"parse_uncertain":0,"unresolved":0,"verified_exact":30,"verified_fuzzy":21},"total_outbound_references":59},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2604.12152."}