{"as_of":"2026-08-04T20:49:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:5083a0a21125f2dd5a3f2ac3b822198ca484af075aca908eccf1d48fbce1359c","coverage":[{"denominator":71,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":71,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-23T05:02:03.402399Z","state":"measured"},{"denominator":71,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":71,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-04T06:34:03.388597+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/2501.09799/citation-record","integrity":"/paper/2501.09799/integrity","json":"/paper/2501.09799/citation-record.json","paper":"/paper/2501.09799"},"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":"Liang and P.C","venue":null,"work_id":"768049d7-567f-486c-9dfe-2f1058d45503","year":2000},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:d025a3c6e20fa102106bba11e1c0af9b21eaa8dbb8dd81a520dae07cd9a0ceb0","observation_id":"49719e29-6749-470f-be50-dffc0f4b3f8b","resolution":{"observed_at":"2026-05-23T05:02:36.816368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Bernstein, K.F","venue":null,"work_id":"04e5e734-e145-43fd-82df-2b26d7629f0b","year":2004},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:3925eedf0650e83e5c6222338ac67815f2d1528429ae7a1f8a4916eed7197a4f","observation_id":"6ca74a4a-fb52-4394-99e5-f97f79cde6e3","resolution":{"observed_at":"2026-05-23T05:02:36.766898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Ultrafast imaging: principles, pitfalls, solutions, and appli- cations","venue":null,"work_id":"456a3bfb-d5d3-45ee-a215-f21d1076c77c","year":2010},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:ae0b59c0431767b298b41d25cb916884e7f8aa553c0cdc1fb1e9387c83dc1cf9","observation_id":"a8e290dd-27fa-4d7f-9254-a6b5bca90739","resolution":{"observed_at":"2026-05-23T05:02:36.759958Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"SENSE: sensitivity encoding for fast MRI","venue":null,"work_id":"fcfdbf0c-66ac-4a4e-9adc-dadd18cdb35e","year":1999},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:7702ebc2df1bf8ac9a0914cec77b6089541879e9a6168e9f22d80ee8a0d911b2","observation_id":"5215ac53-d0fb-45bf-948c-2ec6bcb9f974","resolution":{"observed_at":"2026-05-23T05:02:36.792218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Generalized autocalibrating partially parallel acquisitions (grappa)","venue":null,"work_id":"348539c3-2026-4f19-a7fe-964e208e2b8a","year":2002},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:323badc69872434698fe2beda9147f2ca7beb9f0b3b3ba0ea055c8d423988368","observation_id":"1e3be4e0-bdb5-4d1e-ae1f-28cd32ea4a7f","resolution":{"observed_at":"2026-05-23T05:02:36.775533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Parallel MRI using phased array coils","venue":null,"work_id":"5bdb3e34-b6ff-49b5-b9b1-d555d67ae2d5","year":2010},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:0bc030db86d5903344b42dcb45cd3f325c3bb93c39c23c4170dbfb725ff6ad11","observation_id":"5d809576-aa0f-4cc2-bf9b-b9167128db5f","resolution":{"observed_at":"2026-05-23T05:02:36.779392Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Parallel MR imaging","venue":null,"work_id":"1bcabd84-bf7c-40bc-afd0-60d1922c0461","year":2012},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:9f27e87c03baf4f7027346ff948f04b879a8c3532dad7721f9b3fd4d067b11d7","observation_id":"7c30b058-8a8f-4ec4-a507-773b02c36c14","resolution":{"observed_at":"2026-05-23T05:02:36.804050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Compressed sensing","venue":null,"work_id":"523635dc-eb11-4e0f-9853-42ee905f4a8d","year":2006},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:37085a0ef2a6f89015e5db179b8382e99fc1783d50f6381cae71a65d59de49a1","observation_id":"753c2992-12a7-4af8-a29a-da8c616fb4c3","resolution":{"observed_at":"2026-05-23T05:02:36.799653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Sparse MRI: The application of compressed sensing for rapid MR imaging","venue":null,"work_id":"2c06388b-03c4-4fc6-9f97-e1bea4ae1c92","year":2007},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:f262670e712a8e79eec3f1683a1a03d3c36dedeeab95607bb691540dab2e2dca","observation_id":"6e904e63-dcc7-4044-b59e-6699913cedb3","resolution":{"observed_at":"2026-05-23T05:02:36.825784Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Compressed sensing MRI","venue":null,"work_id":"d42f2c48-2384-418e-b550-e964a7424de7","year":2008},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:9dc88219e1de7c950193ab4c99b3a07de960e8e666edce485bb44d2dc1b9cd7b","observation_id":"5452f2bd-5279-475f-80e3-c763bea63955","resolution":{"observed_at":"2026-05-23T05:02:36.808085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"MR image reconstruction from highly undersampled k-space data by dictionary learning","venue":null,"work_id":"375f3ef0-c128-43cd-b87b-6372d05375ca","year":2010},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:c9597284fecffac8ea63bec23e2955c3f3a317a91a4bc13c742bab1125e94353","observation_id":"a78db4d6-4951-42d4-8259-491e5b884020","resolution":{"observed_at":"2026-05-23T05:02:36.820637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Blind compressive sensing dynamic MRI","venue":null,"work_id":"09c41ed6-dcb4-4048-a6ca-3c0c568d0aa8","year":2013},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:e13cdedc12aaa4f41680bfe86b8c9e35ea8651b9562a34630d1f912c24ad64a6","observation_id":"dc5e5616-fb7e-4f26-a65d-52cc95a772b2","resolution":{"observed_at":"2026-05-23T05:02:36.763294Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator","venue":null,"work_id":"caa389e3-ac96-40a4-b5bc-be9d08ec5261","year":2014},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:0fcd69cce439269be3eb3c21413ca66277972197bb7cd36568d5da7e56a94a18","observation_id":"53d4327b-9e42-42da-adbd-882325816324","resolution":{"observed_at":"2026-05-23T05:02:36.771578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Fast multiclass dictionaries learning with geometrical directions in MRI reconstruction","venue":null,"work_id":"c0a5a2ad-a6be-4491-8d0a-5385cd512a19","year":2015},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:b4c16db659cdeebdfa48de86940d05ad02298160d9b91b073200360748d97506","observation_id":"1943e6f2-3e4c-4b4e-ba55-709bda8d4525","resolution":{"observed_at":"2026-05-23T05:02:36.787843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Learning sparsifying transforms","venue":null,"work_id":"0dd69dd0-1a33-4150-b7f2-9b7c76e7b69e","year":2012},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:d6354588ecb721bd4071ccfeee44e9d7cb2bab2bff063cd3f1755ed57c3ae455","observation_id":"bfb38dde-8bad-476d-8e5b-03f7b624fea2","resolution":{"observed_at":"2026-05-23T05:02:36.795539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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 reconstruction: From sparsity to data-adaptive methods and machine learning","venue":null,"work_id":"c88af1d3-3ae5-48b1-96a5-7479547d3605","year":2019},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:220b0deea0af639ea93fc79b395d8cc735f3516a5278edb1984a07505ea0e765","observation_id":"5f78adb4-934c-4fd2-b550-25cb8a7bd5a7","resolution":{"observed_at":"2026-05-23T05:02:36.783211Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Deep ADMM-Net for compressive sensing MRI","venue":null,"work_id":"b623112c-19a9-435c-9177-e2fc9c115b8d","year":2016},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:f4602f8146aa4aebe24b5f87cd6109f1f63c0f022c115077a1a776765f38201e","observation_id":"f56658ff-06c0-4f37-abfb-e149c6d20c11","resolution":{"observed_at":"2026-05-23T05:02:36.812482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1705.06869","last_updated":"2017-05-19T06:33:18Z","snapshot_observed_at":"2026-07-06T05:43:19.940480Z","submitted_at":"2017-05-19T06:33:18Z","title":"ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI","version":1},"cited_work":{"arxiv_id":"1705.06869","doi":null,"metadata_source":"pith","pith_arxiv_id":"1705.06869","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI","venue":"cs.CV","work_id":"38d18c86-43f4-42e8-82c2-2202a878422e","year":2017},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"cited_paper":"/paper/1705.06869","citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:9cb7756aa85264b7ff5c77f2195bf7282168bced85caeae9d28943c434bce591","observation_id":"9d5c2bb4-f96f-491f-a076-b5a311e34ed8","resolution":{"observed_at":"2026-05-23T05:02:35.890151Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Ista-net: Interpretable optimization- inspired deep network for image compressive sensing","venue":null,"work_id":"f504b2aa-a2fe-4bf0-89b0-0bfd4badc4a8","year":2018},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:426133b6612cbe849f9e7415420c66069240acd76134d4e6b4ef5b05462e3b0c","observation_id":"8a95499c-1f6d-4e01-bdf6-c0413618bda9","resolution":{"observed_at":"2026-05-23T05:02:36.737715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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 segmentation","venue":null,"work_id":"ca72bfbb-4f65-4b1d-8f20-55b0af931154","year":2015},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:9c525b08af99f161608ac9a3f14687f61706ce75d137eca114e74817c357cefe","observation_id":"58b7b9a7-8061-4f8a-87a1-e51a1eced7fd","resolution":{"observed_at":"2026-05-23T05:02:36.741543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Deep learning for undersampled MRI reconstruction","venue":null,"work_id":"ffaba165-2720-47e6-830b-84aca9d23558","year":2018},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:96cc0660b2afeaa1714c1aa44ebaa934f5d1184061981b13da664a3fc75620eb","observation_id":"4826351b-684a-485e-9649-938ced2928aa","resolution":{"observed_at":"2026-05-23T05:02:36.747014Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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 reconstruction by domain-transform manifold learning","venue":null,"work_id":"e00ae64b-3e12-4cd7-8426-212b83aa3199","year":2018},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:d98abee185ccf7c9f25b658d3cb7f27c17430aaffd2e0263a0a06e5d6d544eaf","observation_id":"be8e50ea-a20c-4c5e-9754-1758e317d6f7","resolution":{"observed_at":"2026-05-23T05:02:36.751156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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 reconstruction is a new frontier of machine learning","venue":null,"work_id":"495e38ca-1da4-4584-9dbd-f7c2d2a92474","year":2018},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:91a4e2c28a1403bfbaa7ef319ee901aea852b87c01246c2f35a2d94000621000","observation_id":"f4263120-e26f-4eb3-89b2-fe060b3d2f2a","resolution":{"observed_at":"2026-05-23T05:02:36.714677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues","venue":null,"work_id":"be22f7a1-2fbf-40ad-a96b-4ee42f04330f","year":2020},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:177cdbcd840c9cee7286f18b83a1e96763733a6213e2f714234df0afcf4bbd4c","observation_id":"b27c550c-16f7-4f25-8aab-b6da1c057c0c","resolution":{"observed_at":"2026-05-23T05:02:36.705492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Learning a variational network for reconstruction of accelerated MRI data","venue":null,"work_id":"33870cfa-1a7f-4147-b03e-7acfec345917","year":2018},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:f4d476f33a6e8e8f184b33babf8061a8e2a18b0a89005bd82aa4b1328876658c","observation_id":"280ce613-7cd3-4afe-b938-40bbd3aa856c","resolution":{"observed_at":"2026-05-23T05:02:36.710575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"End-to-end variational networks for acceler- ated MRI reconstruction","venue":null,"work_id":"ce0f5ebf-e016-4d6e-b946-5c8d32f2e1c1","year":2020},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:4cbb7bca4a63cfa826ef3771e46e592152b8402840ad5710920ce8f353cd9fd5","observation_id":"fc630d0c-30e3-4b9d-981a-d09f36a30ceb","resolution":{"observed_at":"2026-05-23T05:02:36.603047Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.00051","last_updated":"2017-05-31T19:12:14Z","snapshot_observed_at":"2026-07-06T05:45:05.277611Z","submitted_at":"2017-05-31T19:12:14Z","title":"Deep Generative Adversarial Networks for Compressed Sensing Automates MRI","version":1},"cited_work":{"arxiv_id":"1706.00051","doi":null,"metadata_source":"pith","pith_arxiv_id":"1706.00051","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Deep Generative Adversarial Networks for Compressed Sensing Automates MRI","venue":"cs.CV","work_id":"6554d5cc-1f80-4302-955e-d9a2e8c2168c","year":2017},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"cited_paper":"/paper/1706.00051","citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:2dcfed92189d2d28beac48cffe58ad89293762a3fabb931d0606047d4d7dffcc","observation_id":"26277f9e-30de-4f1e-838f-758622778b96","resolution":{"observed_at":"2026-05-23T05:02:35.874485Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Dagan: deep de-aliasing generative adversarial networks for fast compressed sensing MRI reconstruction","venue":null,"work_id":"46f80557-34a2-443d-9678-870fe38205af","year":2017},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:b854627a1762f762aa0e9db252cd6a1b363768f5437e0f56514e63c07dc471f5","observation_id":"e07585b6-28b3-4617-8c07-e38ffe2eb537","resolution":{"observed_at":"2026-05-23T05:02:36.584374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"MoDL: Model-based deep learning architecture for inverse problems","venue":null,"work_id":"6c18c0fa-ae96-4966-86fd-3e3566834fe4","year":2018},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:eec2467788cbd3eff7cbab9902375829a796c08ea2f1622f25ccf1e24cd9bda2","observation_id":"f27dbaad-c036-4544-97cb-f700a194ade8","resolution":{"observed_at":"2026-05-23T05:02:36.701125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"J-MoDL: Joint model-based deep learning for optimized sampling and reconstruction","venue":null,"work_id":"7ff4eb5f-8124-4c58-bcb7-d9a19adedf4d","year":2020},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:351984fce2c654e6ece92e2854fe06c552bb897107743d5000b6afea49bc5be3","observation_id":"9df3b2d6-3702-48b9-a3a4-2b60304e8dd1","resolution":{"observed_at":"2026-05-23T05:02:36.686251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Compressed sensing in dynamic MRI","venue":null,"work_id":"bd6c81a9-40c7-4e02-b012-aa87de690bea","year":2008},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:1f6509889910f374e1b0c8b7119967d74d35c33387ce50539503cc70e5658f03","observation_id":"09deb823-57eb-4fd8-a970-16ebb85f10c2","resolution":{"observed_at":"2026-05-23T05:02:36.696986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Compressed-sensing MRI with random encoding","venue":null,"work_id":"a0687552-97e5-4021-a2c8-0f76ab97fac9","year":2010},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:b8857db1ab64bb56deb69e362b054aac6bccb5dcd280a1787b21979550d92bfa","observation_id":"c3acbfa6-0013-41f3-bc09-c5844df14cee","resolution":{"observed_at":"2026-05-23T05:02:36.690055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Fast poisson disk sampling in arbitrary dimensions","venue":null,"work_id":"4ed8be26-e70e-4b72-abf0-d40822f75c6b","year":2007},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:3f847d56a35ae4462a3bbf67916f6a33dfe77fb789f1e2a5b5c896ddd0a04b3e","observation_id":"33f83323-6a64-4dc1-a699-dbd8e00fa01f","resolution":{"observed_at":"2026-05-23T05:02:36.614474Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Spirit: iterative self-consistent parallel imaging reconstruction from arbitrary k-space","venue":null,"work_id":"9d0c485d-2bdc-4c2e-a643-57cfc73d19ff","year":2010},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:35dc8182de0cba97d7aa0381ce4d5c988276f12d788d9c71437f3c4b47d31ed1","observation_id":"e0b2df1c-9214-48a6-8c6a-ac618c3691cc","resolution":{"observed_at":"2026-05-23T05:02:36.718947Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"3D cartesian MRI with compressed sensing and variable view sharing using complementary poisson-disc sampling","venue":null,"work_id":"454396f3-72fa-4b9e-8f75-c8cec8a3a930","year":2017},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:1c38ca92d507ef9f7bd4e4606b0af795d3c24225c6631cb5e533e6c5f4f0fa09","observation_id":"fa676d6d-1200-419d-b742-a0ce27f62e7c","resolution":{"observed_at":"2026-05-23T05:02:36.621910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Adaptive sampling design for com- pressed sensing MRI","venue":null,"work_id":"e1e7f4b1-b9b8-490e-860c-97a0ba9c349d","year":2011},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:fb745798333bb086ae5dcd175675d6215e58bb6d07acb29a8b44f240e41ddcd4","observation_id":"c82486ed-b8b2-42f8-b911-52b88bd11e2f","resolution":{"observed_at":"2026-05-23T05:02:36.633199Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Adapted random sampling patterns for accelerated MRI","venue":null,"work_id":"05a64ec9-d5bc-49f0-9e03-0f2f083fcf08","year":2011},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:d366b46660f4d25f541012072040f3bcc1675bcbe32a8f394954911896b8c3bd","observation_id":"9f572e8e-6f09-4f6b-825e-1e2f58194b12","resolution":{"observed_at":"2026-05-23T05:02:36.682584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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 robust adaptive sampling method for faster acquisition of MR images","venue":null,"work_id":"786e1d11-b398-4df3-9e92-e30022d0c4d5","year":2015},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:88f36b643a8c1c7cd048e0df15e828165e43f44247903219ce1bb4b29512c398","observation_id":"ec03098e-7295-4b9a-afa5-772444ed5126","resolution":{"observed_at":"2026-05-23T05:02:36.617917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Energy preserved sam- pling for compressed sensing MRI","venue":null,"work_id":"8d78b467-30a0-4bd2-93dd-3fab53bdfe59","year":2014},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:977d5c2463d14b3f8a67c4d02d23036123585ec2ca013e432d76210e6e726586","observation_id":"343dc581-e35f-439b-b475-777091b90836","resolution":{"observed_at":"2026-05-23T05:02:36.625637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"OEDIPUS: An experiment design framework for sparsity-constrained MRI","venue":null,"work_id":"0db46093-7e46-4fb2-80a1-d8b6fb43ecea","year":2019},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:2e54f5d6e727618502d07ba92d22b0273174ae7e26954d597297b57c9a37b0a0","observation_id":"808e721b-5ba6-4d32-af4e-74141e9b4ece","resolution":{"observed_at":"2026-05-23T05:02:36.674830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Op- timization of k-space trajectories for compressed sensing by bayesian experimental design","venue":null,"work_id":"c8d70e11-c7b8-4eb8-b5cc-7527f555db78","year":2010},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:850e33868a1ad5da775e5de45d7ac41ed2c07bb2e21ce12f16e176e49de400b0","observation_id":"4848727e-364a-4786-9f8f-cb63c78f6f47","resolution":{"observed_at":"2026-05-23T05:02:36.678714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Learning-based compressive MRI","venue":null,"work_id":"cf05bfd7-d200-4f83-80db-81f8a8e40c7a","year":2018},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:cb561ba08583c73a168333b25c2fa2e9cd42593bc9ec5dcd51192e5e1c79ebc6","observation_id":"81016a0c-4a55-497d-9b40-dcdf2f97b35b","resolution":{"observed_at":"2026-05-23T05:02:36.733518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Rethinking sampling in parallel MRI: A data-driven approach","venue":null,"work_id":"1ea8e596-94bc-479d-9a5b-adb2349481f4","year":2019},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:f9a6a459f9bba88e998f481164ca484d2acb6ae581a4294e205dd97c5eef6f99","observation_id":"62daeb82-9115-42c1-97ba-38e2a3a603ca","resolution":{"observed_at":"2026-05-23T05:02:36.663438Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Scalable learning-based sampling optimization for compressive dynamic MRI","venue":null,"work_id":"3f752054-6ae0-491c-adaa-617c23e55d76","year":2020},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:2a8e2d851e2863dd82257b9a78221946c86c41f91930908361c07039eb7e5ba4","observation_id":"8805befa-2713-41b1-9425-c3dad371060c","resolution":{"observed_at":"2026-05-23T05:02:36.659675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Fast data-driven learning of parallel MRI sampling patterns for large scale problems","venue":null,"work_id":"91b03c71-33f1-46f1-b163-f1d774b97f73","year":2021},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:1e3d0a6bc1fed5156ced4d9b1aa94dcc9b31a7288f22670a604a0a733812c51c","observation_id":"281c0b98-71a8-42f0-ad8b-a391002ccfb0","resolution":{"observed_at":"2026-05-23T05:02:36.667148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Alternating learning approach for variational networks and undersampling pattern in parallel MRI applications","venue":null,"work_id":"6081af33-e69d-4ae0-b334-1ab7fbffb47b","year":2022},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:6d082b86e7da1cd9657939088c1083d4b4f1f9754d2f51bc836e5d06369903ff","observation_id":"7e98aeb6-c49f-4ed3-a54a-87f39c62ef43","resolution":{"observed_at":"2026-05-23T05:02:36.671168Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Deep- learning-based optimization of the under-sampling pattern in MRI","venue":null,"work_id":"10eec8f1-02d9-4992-94c2-3bc66ac3d9fc","year":2020},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:cd09451c0b95efd248cf9314c46150a7e63b592a275e3529135e9d3411cb301a","observation_id":"f4a09f9d-efcb-4d05-b31d-ef6ddc96ce7e","resolution":{"observed_at":"2026-05-23T05:02:36.599228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Extending loupe for k-space under-sampling pattern optimization in multi-coil mri","venue":null,"work_id":"28adaf2c-050f-4822-a5cf-5ff5e2199544","year":2020},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:82aeb5b767683621c29a3da538e6278519319b71ba5e19f1c68e209c1418921a","observation_id":"4d08a857-7b1f-4cce-803c-50f5114f379e","resolution":{"observed_at":"2026-05-23T05:02:36.648465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Learning the sampling pattern for MRI","venue":null,"work_id":"e1d30f1f-c335-4e47-9ed6-7bcaea212666","year":2020},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:9fe44d8d17c12cf3fd2982a0ee8942db5c7cc98963f056bd5a9039f068591046","observation_id":"07adec0d-e199-4fc4-b369-655cc71f3dc6","resolution":{"observed_at":"2026-05-23T05:02:36.652690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"End- to-end sequential sampling and reconstruction for MR imaging","venue":null,"work_id":"9aa0ed03-fb7a-4517-a053-75fe7b4eac16","year":2021},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:7b11dc6567052812a18d1b82b86186b48b6fa745ad5bae4ea7eac317a4ed1746","observation_id":"40af2c39-c572-400b-a25a-f22b9e49c461","resolution":{"observed_at":"2026-05-23T05:02:36.641340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Single-pass object-adaptive data undersampling and reconstruction for MRI","venue":null,"work_id":"4d1420ad-7364-4e28-b2d1-d5e823856e39","year":2022},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:42b4d10f7823e99ab2ddc87742580ff647b13f940b4dc94dbd7705607c93ec37","observation_id":"b483c8cb-f584-409f-994e-621f7f58f325","resolution":{"observed_at":"2026-05-23T05:02:36.644733Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Autosamp: Autoencoding k-space sampling via variational information maximization for 3D MRI","venue":null,"work_id":"e1f80913-e2ad-42e4-a7f4-496d494049ae","year":2024},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:057f024f6841c1e44b4e0c3f35a0d87d8c13ebea71ce66f0dbfbe5cacdb33d02","observation_id":"f45ac697-dc55-4d8d-840c-1deb139ea527","resolution":{"observed_at":"2026-05-23T05:02:36.656224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1909.05773","last_updated":"2021-04-13T06:02:39Z","snapshot_observed_at":"2026-07-06T08:21:16.758909Z","submitted_at":"2019-09-12T16:10:31Z","title":"PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI","version":5},"cited_work":{"arxiv_id":"1909.05773","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1909.05773","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Pilot: Physics-informed learned optimized trajectories for accelerated mri","venue":null,"work_id":"e84c501b-55f4-4dfb-bdfe-9d933a80ebec","year":1909},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"cited_paper":"/paper/1909.05773","citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:f0dc47b2c68b1c221b2e64f384b2000481cfd2263c317114bcb83f927bc17bf8","observation_id":"345f42e2-aa4b-407e-94da-52d7a5b098fa","resolution":{"observed_at":"2026-05-23T05:02:35.879811Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Sparkling: variable-density k-space filling curves for accelerated T2*-weighted MRI","venue":null,"work_id":"dc99b8b6-e4f9-444b-b299-17092048d54d","year":2019},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:41aafd5e16a0116ac012f488d8d2e98fde947dd2bd510b41447050b6a78978f6","observation_id":"0550e514-d596-4f64-ba95-0b329a0b31f7","resolution":{"observed_at":"2026-05-23T05:02:36.637086Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Optimizing full 3D sparkling trajectories for high-resolution magnetic resonance imaging","venue":null,"work_id":"7d8fa950-f366-42d4-afb4-580f0b4ef5ce","year":2022},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:82bb162843210eda071368e20604acad306b8f9c6d9f3e0142a4db99099f298b","observation_id":"8bac663b-17e1-4174-a5aa-d94684a6808d","resolution":{"observed_at":"2026-05-23T05:02:36.606731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"B- spline parameterized joint optimization of reconstruction and k-space trajectories (BJORK) for accelerated 2D MRI","venue":null,"work_id":"40e66339-a8e4-4d68-b363-12613ea31018","year":2022},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:0946874ae55c60e3fe0f27a470ffa0ecb9b8186a2a5ef578b4ff06ce02783871","observation_id":"69af39fd-63b0-4034-87cf-6277a74aefcd","resolution":{"observed_at":"2026-05-23T05:02:36.610966Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"NC-PDNet: A density-compensated unrolled network for 2D and 3D non-cartesian MRI reconstruction","venue":null,"work_id":"87cf636b-634e-4f32-94e1-12a9883d6b2c","year":2022},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:ff42a8751d0be18135e72ff838e5bc0567cb7711f0ff29f934dee9f5978d84f3","observation_id":"d776a3cf-ca32-4fa1-ae2d-29a323509610","resolution":{"observed_at":"2026-05-23T05:02:36.592121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Stochastic optimization of three-dimensional non-cartesian sampling trajectory","venue":null,"work_id":"dd58569c-0556-4ca2-be5d-1d89b0498244","year":2023},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:ea65af3d5ab0efe604aa895c3d3c3061e09571af208a2d7bdaf5070d335b2256","observation_id":"68726dca-0731-4f4d-a0c0-15a51fa19f02","resolution":{"observed_at":"2026-05-23T05:02:36.629629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Active MR k-space sampling with reinforcement learning","venue":null,"work_id":"61438623-bd59-4d0d-aa88-2e34520fd294","year":2020},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:afa83ea4f772c8174592aa6bd9314db099c357899d49c7d968c6c8f44e47517a","observation_id":"54323192-6c21-481b-98fc-38db9c776853","resolution":{"observed_at":"2026-05-23T05:02:36.595428Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Experimental design for MRI by greedy policy search","venue":null,"work_id":"3ed79e8a-779f-4a4b-b852-23ee5d1d3480","year":2020},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:9cef984b7a4d25fc396a1a2dd5d1c2d379d1be600f52606efa36261647d06b3e","observation_id":"abb7cc52-7f20-40e2-9aad-0e3f5823b38f","resolution":{"observed_at":"2026-05-23T05:02:36.580661Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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","venue":null,"work_id":"7df8a173-0fa2-4021-a293-cbb79b034c2c","year":2004},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:2fbb5a915ea9718d897a8f9fd7ccff942cc90268388a43eb2494a8266eabe773","observation_id":"abdd89f1-acdb-49d6-abaa-acfa2fa14a14","resolution":{"observed_at":"2026-05-23T05:02:36.569623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Patient-adaptive and learned MRI data undersampling using neighborhood clustering","venue":null,"work_id":"d854c105-f77d-48d3-acae-973adcd2146b","year":2024},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:212fd80936dc0d17b1198c384b4085d1d8dde1e8dadd59c8a43ece04a3d1c7b0","observation_id":"9cf1f1e0-4c78-45ff-8d5c-d3c04e0ef9dc","resolution":{"observed_at":"2026-05-23T05:02:36.573436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Zero-shot self- supervised learning for MRI reconstruction","venue":null,"work_id":"1a570d4a-be3f-4488-962a-abc9a34e5de6","year":2022},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:bfb3b284ede8d643f866f791fd1ed3f72d9d4c3ae9d21a74acf0d9954f8b4ffc","observation_id":"2f97bf0e-f2b7-4887-96b1-de9ec20f753b","resolution":{"observed_at":"2026-05-23T05:02:36.566127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.08839","last_updated":"2019-12-11T10:31:39Z","snapshot_observed_at":"2026-08-01T03:38:56.284253Z","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":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"cited_paper":"/paper/1811.08839","citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:382c4f99ea107372fc06f072ee1f48ced39c54c7bcf59a98056ecbba79bf15de","observation_id":"76076dac-8171-45e0-be6a-7338fd951b65","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 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"fastMRI: A publicly available raw k-space and dicom dataset of knee images for accelerated MR image reconstruction using machine learning","venue":null,"work_id":"74073277-1188-49fc-910a-b624f394ded9","year":2020},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:65c421c7b6e8bdf10038a14ab604ac84b03a801422784b51a6b363edbc80773a","observation_id":"b3e6dd3d-245b-45de-89a1-64d78be7414f","resolution":{"observed_at":"2026-05-23T05:02:36.577075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"ESPIRiT — an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA","venue":null,"work_id":"fd869ccb-03a0-4dc4-84dc-dd5b3dd6a4d8","year":2014},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:eebbf7887d1f60c3561f8681e5e67874841661cc6c21210349098db9dad65a12","observation_id":"2f69314e-1b16-4107-b468-a16ce1779769","resolution":{"observed_at":"2026-05-23T05:02:36.588115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1308.3432","last_updated":"2013-08-15T15:19:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-08-15T15:19:34Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","version":1},"cited_work":{"arxiv_id":"1308.3432","doi":"10.48550/arxiv.1308.3432","metadata_source":"pith","pith_arxiv_id":"1308.3432","snapshot_observed_at":"2026-07-11T03:27:46.813463Z","title":"Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation","venue":"cs.LG","work_id":"1fe8c7c8-aff7-4b94-9096-e549d7e60789","year":2013},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"cited_paper":"/paper/1308.3432","citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:8027687909f2aa057fe64063792ef36467136a84afc504b8f1481840e88f6001","observation_id":"5b7957e6-9c03-49b4-a5f8-b2a3f4030053","resolution":{"observed_at":"2026-05-23T05:02:35.863542Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-05-19T16:22:27.235199+00:00","source":"crossref_status_cache"},{"observed_at":"2026-05-19T16:22:27.235199+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Deep iterative down-up CNN for image denoising","venue":null,"work_id":"04e83638-7b6b-4a57-85b1-e338ca5555f2","year":2019},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:4026916238b1898888cbe3319c8b69aa66bacaf1517f69d05b634b7a93edcb8f","observation_id":"fd1fea76-0f6f-415b-b3d3-1fb03eac9c67","resolution":{"observed_at":"2026-05-23T05:02:36.755891Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":"1412.6980","doi":"10.1002/mrm.28086","metadata_source":"pith","pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-07-11T11:50:26.030339Z","title":"Adam: A Method for Stochastic Optimization","venue":"cs.LG","work_id":"1910796d-9b52-4683-bf5c-de9632c1028b","year":2014},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:35265879707a016c9a04ad1fb8c26027b6487b060f3bc5f01b9a779edd07be93","observation_id":"336f74c6-c794-4d61-a8c6-c5d03394e0c7","resolution":{"observed_at":"2026-05-23T05:02:35.885427Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"Adaptive local neighborhood- based neural networks for MR image reconstruction from undersampled data","venue":null,"work_id":"7e8084d3-0e80-413e-9a3a-7ef3789c65b4","year":2024},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:8ae86119237d4f958cfa05cfbfd207dae53f027cd42b0d6154efb28f424cc212","observation_id":"8f3fd733-b51a-4f56-8d7a-4a16081af5f1","resolution":{"observed_at":"2026-05-23T05:02:36.693616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+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":"fastMRI+, clinical pathol- ogy annotations for knee and brain fully sampled magnetic resonance imaging data","venue":null,"work_id":"4e5e300c-40b3-4d62-9a87-194b3b38f5f9","year":2022},"citing_paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)","version":6},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-05-23T05:02:03.402399Z"},"links":{"citing_paper":"/paper/2501.09799"},"observation_digest":"sha256:d49364b38c81addddf8d7e8d4d356bdc2e2977c6256c2a1ac6c2328289b7d746","observation_id":"dd488a1c-e88c-492c-9788-68b060fe4db8","resolution":{"observed_at":"2026-05-23T05:02:36.722648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.09799","last_updated":"2026-01-15T16:40:54Z","latest_version":6,"primary_category":"eess.IV","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-16T19:03:03Z","title":"Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO)"},"reference_resolution":{"displayed":71,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":6,"verified_fuzzy":65},"total_outbound_references":71},"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":"2026-08-04T06:34:03.388597+00:00","source":"crossref"},{"observed_at":"2026-08-04T06:33:57.428241+00:00","source":"retraction_watch"}],"thesis":"As of 4 August 2026, this Paper Citation Record lists 71 of 71 outbound references and 0 inbound Pith citation observations for arXiv:2501.09799."}