{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TRLEN2EUREFASVRIBVM2LBOYNK","short_pith_number":"pith:TRLEN2EU","schema_version":"1.0","canonical_sha256":"9c5646e894890a0956280d59a585d86abe8466e7e93a1d87a82e48562e23453e","source":{"kind":"arxiv","id":"1910.09116","version":1},"attestation_state":"computed","paper":{"title":"Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","eess.SP","physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Burhaneddin Yaman, Jutta Ellermann, K\\^amil U\\v{g}urbil, Mehmet Ak\\c{c}akaya, Seyed Amir Hossein Hosseini, Steen Moeller","submitted_at":"2019-10-21T02:20:15Z","abstract_excerpt":"Deep learning (DL) has emerged as a tool for improving accelerated MRI reconstruction. A common strategy among DL methods is the physics-based approach, where a regularized iterative algorithm alternating between data consistency and a regularizer is unrolled for a finite number of iterations. This unrolled network is then trained end-to-end in a supervised manner, using fully-sampled data as ground truth for the network output. However, in a number of scenarios, it is difficult to obtain fully-sampled datasets, due to physiological constraints such as organ motion or physical constraints such"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1910.09116","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-21T02:20:15Z","cross_cats_sorted":["cs.CV","cs.LG","eess.SP","physics.med-ph"],"title_canon_sha256":"6241eeb6dfb0ff3f9aca82e4c270578c05fc93ca8503f21d1320a4060b7d37b4","abstract_canon_sha256":"ed33188426a01e2838ac4f2735f86dcb1f3fc0e34d66338ea78e6eb288c27249"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:15:36.647852Z","signature_b64":"Co7jC0n03KmK9KS+TcqlUsa5o8axY+HGr70mFKFuck0FknoAz/BHi6XZY69o16MOms47HkwggUefnV4gOFamCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c5646e894890a0956280d59a585d86abe8466e7e93a1d87a82e48562e23453e","last_reissued_at":"2026-07-05T01:15:36.647355Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:15:36.647355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","eess.SP","physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Burhaneddin Yaman, Jutta Ellermann, K\\^amil U\\v{g}urbil, Mehmet Ak\\c{c}akaya, Seyed Amir Hossein Hosseini, Steen Moeller","submitted_at":"2019-10-21T02:20:15Z","abstract_excerpt":"Deep learning (DL) has emerged as a tool for improving accelerated MRI reconstruction. A common strategy among DL methods is the physics-based approach, where a regularized iterative algorithm alternating between data consistency and a regularizer is unrolled for a finite number of iterations. This unrolled network is then trained end-to-end in a supervised manner, using fully-sampled data as ground truth for the network output. However, in a number of scenarios, it is difficult to obtain fully-sampled datasets, due to physiological constraints such as organ motion or physical constraints such"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.09116","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1910.09116/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1910.09116","created_at":"2026-07-05T01:15:36.647423+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.09116v1","created_at":"2026-07-05T01:15:36.647423+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.09116","created_at":"2026-07-05T01:15:36.647423+00:00"},{"alias_kind":"pith_short_12","alias_value":"TRLEN2EUREFA","created_at":"2026-07-05T01:15:36.647423+00:00"},{"alias_kind":"pith_short_16","alias_value":"TRLEN2EUREFASVRI","created_at":"2026-07-05T01:15:36.647423+00:00"},{"alias_kind":"pith_short_8","alias_value":"TRLEN2EU","created_at":"2026-07-05T01:15:36.647423+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TRLEN2EUREFASVRIBVM2LBOYNK","json":"https://pith.science/pith/TRLEN2EUREFASVRIBVM2LBOYNK.json","graph_json":"https://pith.science/api/pith-number/TRLEN2EUREFASVRIBVM2LBOYNK/graph.json","events_json":"https://pith.science/api/pith-number/TRLEN2EUREFASVRIBVM2LBOYNK/events.json","paper":"https://pith.science/paper/TRLEN2EU"},"agent_actions":{"view_html":"https://pith.science/pith/TRLEN2EUREFASVRIBVM2LBOYNK","download_json":"https://pith.science/pith/TRLEN2EUREFASVRIBVM2LBOYNK.json","view_paper":"https://pith.science/paper/TRLEN2EU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.09116&json=true","fetch_graph":"https://pith.science/api/pith-number/TRLEN2EUREFASVRIBVM2LBOYNK/graph.json","fetch_events":"https://pith.science/api/pith-number/TRLEN2EUREFASVRIBVM2LBOYNK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TRLEN2EUREFASVRIBVM2LBOYNK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TRLEN2EUREFASVRIBVM2LBOYNK/action/storage_attestation","attest_author":"https://pith.science/pith/TRLEN2EUREFASVRIBVM2LBOYNK/action/author_attestation","sign_citation":"https://pith.science/pith/TRLEN2EUREFASVRIBVM2LBOYNK/action/citation_signature","submit_replication":"https://pith.science/pith/TRLEN2EUREFASVRIBVM2LBOYNK/action/replication_record"}},"created_at":"2026-07-05T01:15:36.647423+00:00","updated_at":"2026-07-05T01:15:36.647423+00:00"}