{"as_of":"2026-07-22T02:40:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:53f24b6c10db02c89f5613d4ab8dc6926b78d31b605f143f51799f0c01be24e7","coverage":[{"denominator":33,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":33,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-09T15:26:52.844479Z","state":"measured"},{"denominator":33,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":33,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-07-21T06:31:05.380196+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/2605.01185/citation-record","integrity":"/paper/2605.01185/integrity","json":"/paper/2605.01185/citation-record.json","paper":"/paper/2605.01185"},"outbound":[{"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":null,"venue":null,"work_id":"cc2b6ece-73b8-4801-afa1-50deb6e7ad12","year":2023},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:d2648efdd9a05d3ccde805699f13f5efa280a5ca1950505df3157ce5d3dd5cfa","observation_id":"bbdd2241-a566-42dd-9e4a-a260820e8fae","resolution":{"observed_at":"2026-05-26T01:11:28.283209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Seeing what a gan cannot generate","venue":null,"work_id":"d017ef8a-9017-4b9a-aa78-0d0cd08960c1","year":2019},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:72e84e3718aba857dcf442b6475d4a59cc5dc8d2fe548292439e7be11716a985","observation_id":"edc08b6c-96b6-41ec-9f73-3ff29f360f0c","resolution":{"observed_at":"2026-05-26T01:11:28.275789Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Large scale GAN training for high fidelity natural image synthe- sis","venue":null,"work_id":"5beee348-fdcf-4b0b-ad74-7b0c318b13fe","year":2019},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:6ff6c5713a7924b71a669ccd3a67dbc54000cf29b10e767b12201128fd534d73","observation_id":"09377f0e-84c9-4965-beb4-3669567b74e5","resolution":{"observed_at":"2026-05-26T01:11:28.353777Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Score-based diffusion models for accelerated mri.Medical image analysis, 80: 102479","venue":null,"work_id":"81a797c8-7d1a-4a75-ba1b-4ea5ecef9797","year":2022},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:f9e0a551333ca395cb859aeb43a6ae8d51d3d3e12a112fe6ec7fdcc8023c05a5","observation_id":"e10ea1eb-8ff2-40c6-9f0b-bfffde29014b","resolution":{"observed_at":"2026-05-26T01:11:28.345392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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 3d inverse problems using pre-trained 2d diffusion models","venue":null,"work_id":"baf96737-e844-4add-b311-6d2320b36b0b","year":2023},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:e63b5123188cd84105a2788e4b52cccc2d4b5d725d58d1e06a0e73570b7f41d1","observation_id":"a7f9cbb6-2955-432a-91bf-0d84c6f00369","resolution":{"observed_at":"2026-05-26T01:11:28.357849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Synthesizing complex- valued multicoil mri data from magnitude-only images.Bio- engineering, 10(3):358","venue":null,"work_id":"c679d84e-f5fd-47e8-8b5f-4daa701ab383","year":2023},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:3733f7a9187aef6e09b02f19f7f8394a17cb1b0dbedf4c80c17feac6a2afdbef","observation_id":"848b0f59-11db-4fcd-9f55-3fc42c180b81","resolution":{"observed_at":"2026-05-26T01:11:28.328839Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Diffusion models beat gans on image synthesis.Advances in neural informa- tion processing systems, 34:8780–8794","venue":null,"work_id":"58d4b944-3e89-4f62-a297-5936efd3ab01","year":2021},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:c96339526f50d2b27f8de53a7a23f3eba42ce0c8255d39af025368f6a550bc7e","observation_id":"a4ac9367-8668-46e8-a465-15e1b983c374","resolution":{"observed_at":"2026-05-26T01:11:28.337155Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Synthetic data accelerates the development of gener- alizable learning-based algorithms for x-ray image analysis","venue":null,"work_id":"6861d0fc-640e-4b33-b8ec-ff41a1852dbf","year":2023},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:38e42f27446171085998d2ca19bf90d6d920890624ae06eb16e170cdfa585a48","observation_id":"96016f1a-b78f-4f11-b84a-fd680f70fd23","resolution":{"observed_at":"2026-05-26T01:11:28.370849Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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 adversarial networks.Commu- nications of the ACM, 63(11):139–144","venue":null,"work_id":"2e3d47d8-7b76-4d66-adbc-0a2dce2b536f","year":2020},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:48d4831f8e925177f50eb4831d60016776797836516c5a746af96129ed000ee9","observation_id":"482c749f-59d3-4f39-bce8-cc1ccea55a44","resolution":{"observed_at":"2026-05-26T01:11:28.366577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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 equilib- rium.Advances in neural information processing systems, 30","venue":null,"work_id":"064aeac6-239c-4500-823d-a15c8faa2040","year":2017},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:a2b375907a060f7274414e0a3f5d3992e38f93aa72679a9d4d1f08e898faac06","observation_id":"185a624b-ede3-453b-af09-c2925afaa477","resolution":{"observed_at":"2026-05-26T01:11:28.319780Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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 dif- fusion probabilistic models.Advances in neural information processing systems, 33:6840–6851","venue":null,"work_id":"c170d354-ba68-471f-82ff-289b91eb5d87","year":2020},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:1d3fe1b580179ef4b704b5846ebf78bea5b47994ac83ab048477993abfdd9472","observation_id":"e75c12b7-1ad0-4451-899a-5937527d0213","resolution":{"observed_at":"2026-05-26T01:11:28.362292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Image-to-image translation with conditional adver- sarial networks","venue":null,"work_id":"63f13b6d-d1a6-430d-ba79-ec59e280eff0","year":null},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:b7a7c4f4fff588fe1065f87886b2aaee229cfb464dc54b5d7644276464ccd382","observation_id":"57dc46c3-a419-4c19-8a2a-dbe36a9f071f","resolution":{"observed_at":"2026-05-26T01:11:28.388343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Cola-diff: Conditional latent diffusion model for multi- modal mri synthesis","venue":null,"work_id":"84301345-467d-47f7-8a96-071e31833f6d","year":2023},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:32c4983dd9b5b19788e9ba83ef8c4a7c2e7ce270214239302def225e66ad5a54","observation_id":"2481d8e0-78e3-418b-97ef-b766fd22c567","resolution":{"observed_at":"2026-05-26T01:11:28.315454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"A style-based generator architecture for generative adversarial networks","venue":null,"work_id":"505e85d6-ba08-4bd3-9b66-48d288eadbd4","year":2019},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:e62f09bb7daa079b709145b6bfbd7200f30f1079234dd28a3fb08f2a22fe73c9","observation_id":"5eeb37b9-3e7d-4fc1-994b-37940bb9dc09","resolution":{"observed_at":"2026-05-26T01:11:28.324553Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Analyzing and improving the image quality of stylegan","venue":null,"work_id":"2639ea01-b310-4ddb-9c7a-83d128fff5d7","year":2020},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:b7f1745618ef6db3a6da169772efae85fd9025e6e93316d9e81ca87c5ddb16cd","observation_id":"94a7c991-45fa-468e-97f8-494cc3f2310f","resolution":{"observed_at":"2026-05-26T01:11:28.332727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Kingma and Jimmy Ba","venue":null,"work_id":"2d09e175-6b0d-4e78-9357-236738aab60b","year":2015},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:9b182dff5d6abd6ef981a5cc9f080f4d0e7a789c9d7efe05503c77c5e7030dab","observation_id":"2f6bfb06-0e4f-4220-95cf-489e7c23def8","resolution":{"observed_at":"2026-05-26T01:11:28.341291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":null,"venue":null,"work_id":"3c18759f-0e25-47bd-a020-0ec490b6e9d0","year":2020},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:7a30e6d3c64eaf8d93d615b1e2202d9646c938063754d4e7ecfb13dce767dee3","observation_id":"4f79532b-c727-494f-b0b3-4b73ad408ba0","resolution":{"observed_at":"2026-05-26T01:11:28.349571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":null,"venue":null,"work_id":"e1571551-9464-491b-bd7b-18b48f79413f","year":2022},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:c735dcbc765739b710eede0ce2d0fb898b3a773079bf57c36eac2e6f7987e4dd","observation_id":"e3c87c3e-5cb9-42aa-b337-dd33a0276e97","resolution":{"observed_at":"2026-05-26T01:11:28.294994Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"A multimodal comparison of latent denois- ing diffusion probabilistic models and generative adversarial networks for medical image synthesis.Scientific Reports, 13 (1):12098","venue":null,"work_id":"f5621971-5767-415d-8fc2-190575d449f4","year":2023},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:fe5792104fd036839cca3fd03f92b2e3f8a3875cb650e93b8490740d77a57e56","observation_id":"21d1e992-9168-45d7-812f-a62dfcd30a33","resolution":{"observed_at":"2026-05-26T01:11:28.383878Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.08034","last_updated":"2023-09-07T15:07:01Z","snapshot_observed_at":"2026-07-06T14:31:07.280398Z","submitted_at":"2022-12-15T18:45:13Z","title":"Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Model","version":2},"cited_work":{"arxiv_id":"2212.08034","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2212.08034","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Generating realistic 3d brain mris using a conditional diffu- sion probabilistic model.arXiv preprint arXiv:2212.08034","venue":null,"work_id":"63d94304-e4f8-42e1-8038-2722e8047450","year":null},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"cited_paper":"/paper/2212.08034","citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:b1832950ae279321f50f9ab6ce6ee7aa978e8a962f8374289b4e85dbb677596f","observation_id":"54381ba8-eb9c-4937-b4ee-51aa27646e25","resolution":{"observed_at":"2026-05-11T16:41:12.108390Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Da Costa","venue":null,"work_id":"8daf690b-0c18-4d42-98c9-525eb958be0b","year":2022},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:3ee6aec5bff9ea5300f4e3906e8bd6ae734a4655d69fcbc0eef3097e743f4261","observation_id":"d8c72c0c-53de-460d-8197-f511ce7aa530","resolution":{"observed_at":"2026-05-26T01:11:28.298834Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"2026-07-05T06:40:46.053221Z","title":"High-resolution image synthesis with latent diffusion models","venue":null,"work_id":"8e9db76b-fe9d-4487-bb62-b413b9491305","year":2022},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:e5c3f658fde6660720be4333eb527e35dcb5d072750f451dd654bac8459be9d5","observation_id":"ff586c47-01ef-4760-8ed9-a39fb20cd6fb","resolution":{"observed_at":"2026-05-26T01:11:28.375372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"U- net: Convolutional networks for biomedical image segmen- tation","venue":null,"work_id":"02994ac9-6e0b-43f7-90d2-d6be6e2a2e03","year":2015},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:14914ddf5411524686f4638c9023af6840cced0f9647ca1531fc0ceef2cd470f","observation_id":"4e3f222d-a372-4b52-a656-ce54e167db17","resolution":{"observed_at":"2026-05-26T01:11:28.379517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"pytorch-fid: FID Score for PyTorch","venue":null,"work_id":"1b4e320c-8b78-4c64-bf06-9033b1fe0f8e","year":null},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:53e9949a502241b43fcfc071e99029994a659dd49822ea179fbcb6871ce556a4","observation_id":"f1769783-1fbc-4f18-9af9-34ccedcc777d","resolution":{"observed_at":"2026-05-26T01:11:28.291195Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Improved techniques for training score-based generative models","venue":null,"work_id":"322f78b5-5748-4d11-b8c9-4322b097d383","year":2020},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:bf7daad7384b43d43a68ec37c9dcaaedf6b36e24cb8d2cf2829f73e9d0a08d66","observation_id":"aabe0e15-7b0b-47cd-bf1d-58d6cf364406","resolution":{"observed_at":"2026-05-26T01:11:28.303075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Kingma, Ab- hishek Kumar, Stefano Ermon, and Ben Poole","venue":null,"work_id":"ea4cd6ac-ccb1-4c46-8097-0c9e9a976e9b","year":2021},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:77eb495eee6eef52fcaeafeee1214114286e4caff14da7f7e447fae9788c49f2","observation_id":"cfc40bdb-2481-4bdf-8dca-d190ae4cf438","resolution":{"observed_at":"2026-05-26T01:11:28.307222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":null,"venue":null,"work_id":"76822923-cfc0-48a6-b620-3b03bafc003f","year":2020},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:c8ac9382671a0aadbe084f539b5412142db9be5b00cb1607259f5f12d2771e40","observation_id":"260afda3-aa57-4090-b7f4-ed56d212ff1b","resolution":{"observed_at":"2026-05-26T01:11:28.279479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Simulating single-coil mri from the responses of multiple coils.Communications in Ap- plied Mathematics and Computational Science, 15(2):115– 127","venue":null,"work_id":"50e6eb47-f8f9-408d-b71c-66d4ff67b78a","year":2020},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:6911054827237be5f4c7b1d3dd60b7b3f6c5bcc2eec66a0b79019d4d63709ee6","observation_id":"bab88bd2-5a4a-4a5a-ae15-51abf6963526","resolution":{"observed_at":"2026-05-26T01:11:28.272073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Brain tumor segmentation using synthetic mr images-a comparison of gans and diffusion models.Sci- entific Data, 11(1):259","venue":null,"work_id":"c39d6c1a-1bc2-49b6-84c0-8b2bb5b669c5","year":2024},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:be8ad8227273fbbd8d6e895343488d3436f5f11e41f3e8630cfafaca21e4fa08","observation_id":"3dfd97c9-4927-4b81-aa58-625055ebe3a9","resolution":{"observed_at":"2026-05-26T01:11:28.287385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"A connection between score matching and denoising autoencoders.Neural Computation, 23(7):1661– 1674","venue":null,"work_id":"5b12b0c8-098d-4615-b759-1cf0231e4bd8","year":2011},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:992ae79c7a554049fa8441b380b9a88b561e22f92be68dd46f8fea50ce5ffd05","observation_id":"f2f6e72c-79b7-496e-bdbc-b07987acd865","resolution":{"observed_at":"2026-05-26T01:11:28.311370Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+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":"Image quality assessment: from error visibility to structural similarity.IEEE transactions on image processing, 13(4):600–612","venue":null,"work_id":"e0a7873c-23fd-4b1b-a555-fa2d012e1b9b","year":2004},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:8aa2ba96634d62360c8a996f394566f7541f0214b855134274ef69aa7b3e8bda","observation_id":"2ca5d809-8f1f-4459-9c81-459e0b428a3a","resolution":{"observed_at":"2026-05-26T01:11:28.267689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.13220","last_updated":"2024-02-28T11:56:57Z","snapshot_observed_at":"2026-07-06T15:58:08.777051Z","submitted_at":"2023-07-25T03:11:24Z","title":"One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction","version":2},"cited_work":{"arxiv_id":"2307.13220","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2307.13220","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"One for multiple: Physics-informed synthetic data boosts generaliz- able deep learning for fast mri reconstruction","venue":null,"work_id":"a8af932a-0b02-452e-920f-322b8161db14","year":2023},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"cited_paper":"/paper/2307.13220","citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:fb94aafe8b9146f33c2e0419b9a21603b85f0d5bd5de087585487cceeaf51d4a","observation_id":"c050cf1c-e852-4642-93b0-530f844ccea0","resolution":{"observed_at":"2026-05-11T16:41:12.104339Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.08839","last_updated":"2019-12-11T10:31:39Z","snapshot_observed_at":"2026-07-06T07:16:17.828298Z","submitted_at":"2018-11-21T17:32:14Z","title":"fastMRI: An Open Dataset and Benchmarks for Accelerated MRI","version":2},"cited_work":{"arxiv_id":"1811.08839","doi":"10.1038/s41467-021-25431-8","metadata_source":"pith","pith_arxiv_id":"1811.08839","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"fastMRI: An Open Dataset and Benchmarks for Accelerated MRI","venue":"cs.CV","work_id":"1935feba-42c8-49e7-8dcd-5bae11b83a60","year":2018},"citing_paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-09T15:26:52.844479Z"},"links":{"cited_paper":"/paper/1811.08839","citing_paper":"/paper/2605.01185"},"observation_digest":"sha256:bd4087cbb75611f654aebac7a6f94fce3acf65c7360eaa4de36a5e0a2c4c3a50","observation_id":"0e62122c-4be0-4627-9bf0-018b61a525ac","resolution":{"observed_at":"2026-05-23T22:06:02.657571Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.01185","last_updated":"2026-05-02T01:25:43Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-07-06T23:14:29.125042Z","submitted_at":"2026-05-02T01:25:43Z","title":"Phase-map synthesis from magnitude-only MR images using conditional score-based diffusion models with application in training of accelerated MRI reconstruction models"},"reference_resolution":{"displayed":33,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":3,"verified_fuzzy":30},"total_outbound_references":33},"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":"2026-07-21T06:31:05.380196+00:00","source":"crossref"},{"observed_at":"2026-07-21T06:31:00.184556+00:00","source":"retraction_watch"}],"thesis":"As of 22 July 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2605.01185."}