{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:I3PJWLKGUHV33SEJGUMQCLQUXI","short_pith_number":"pith:I3PJWLKG","canonical_record":{"source":{"id":"2110.00075","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-09-30T20:06:43Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"ace6c3068c897b6c8855a1941775eff858eb72765619556c37b0d656f1f411fb","abstract_canon_sha256":"d602d1d6a08d90a5ba1be7c073afc6fb279e022f94e4f33c5d1226e45b724876"},"schema_version":"1.0"},"canonical_sha256":"46de9b2d46a1ebbdc8893519012e14ba184d9c7a95f15a1969bf782e17916661","source":{"kind":"arxiv","id":"2110.00075","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.00075","created_at":"2026-07-05T05:04:09Z"},{"alias_kind":"arxiv_version","alias_value":"2110.00075v2","created_at":"2026-07-05T05:04:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.00075","created_at":"2026-07-05T05:04:09Z"},{"alias_kind":"pith_short_12","alias_value":"I3PJWLKGUHV3","created_at":"2026-07-05T05:04:09Z"},{"alias_kind":"pith_short_16","alias_value":"I3PJWLKGUHV33SEJ","created_at":"2026-07-05T05:04:09Z"},{"alias_kind":"pith_short_8","alias_value":"I3PJWLKG","created_at":"2026-07-05T05:04:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:I3PJWLKGUHV33SEJGUMQCLQUXI","target":"record","payload":{"canonical_record":{"source":{"id":"2110.00075","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-09-30T20:06:43Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"ace6c3068c897b6c8855a1941775eff858eb72765619556c37b0d656f1f411fb","abstract_canon_sha256":"d602d1d6a08d90a5ba1be7c073afc6fb279e022f94e4f33c5d1226e45b724876"},"schema_version":"1.0"},"canonical_sha256":"46de9b2d46a1ebbdc8893519012e14ba184d9c7a95f15a1969bf782e17916661","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:04:09.840920Z","signature_b64":"j1gfJLM04z8iWrrqC3MvPCecezGa/JOdSrDJkVNgt0lE95j6Y5xrxBiFqOUR36COlIS0V5Gk9jNG7X9S3QNuCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46de9b2d46a1ebbdc8893519012e14ba184d9c7a95f15a1969bf782e17916661","last_reissued_at":"2026-07-05T05:04:09.840496Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:04:09.840496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2110.00075","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:04:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zuZfhBUSmWhKwgeLdWoEknI1bxIk9rt3Tr88Ib/sFJkzHKS346zOsRL1B1nydEt+QAeRViggwwR1B8iSOVTzAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T17:22:55.308562Z"},"content_sha256":"bb2d74c04d21316e89550e1cd0c6d4359b5de7935602ee68de586b690cb3920c","schema_version":"1.0","event_id":"sha256:bb2d74c04d21316e89550e1cd0c6d4359b5de7935602ee68de586b690cb3920c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:I3PJWLKGUHV33SEJGUMQCLQUXI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Akshay S Chaudhari, Arjun D Desai, Batu M Ozturkler, Brian A Hargreaves, Christopher M R\\'e, Christopher M Sandino, John M Pauly, Marc Willis, Robert Boutin, Shreyas Vasanawala","submitted_at":"2021-09-30T20:06:43Z","abstract_excerpt":"Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose Noise2Recon, a model-agnostic, consistency training method for joint MRI reconstruction and denoising that can use both fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised and self-supervised settings. With limited or no labeled traini"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.00075","kind":"arxiv","version":2},"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/2110.00075/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:04:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HSpTQ+JSZrwSchun8XsdoU8dF2+DO0kVEf+s4yrN4sQnzCTxgQPw9f+FaGpNAUTflLNYlrQgjEFTfjHscybHBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T17:22:55.309540Z"},"content_sha256":"cffefaf98bfa91d7eee03f0eb5f2637611db33b955a3416728ae68ecb364940e","schema_version":"1.0","event_id":"sha256:cffefaf98bfa91d7eee03f0eb5f2637611db33b955a3416728ae68ecb364940e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I3PJWLKGUHV33SEJGUMQCLQUXI/bundle.json","state_url":"https://pith.science/pith/I3PJWLKGUHV33SEJGUMQCLQUXI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I3PJWLKGUHV33SEJGUMQCLQUXI/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-19T17:22:55Z","links":{"resolver":"https://pith.science/pith/I3PJWLKGUHV33SEJGUMQCLQUXI","bundle":"https://pith.science/pith/I3PJWLKGUHV33SEJGUMQCLQUXI/bundle.json","state":"https://pith.science/pith/I3PJWLKGUHV33SEJGUMQCLQUXI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I3PJWLKGUHV33SEJGUMQCLQUXI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:I3PJWLKGUHV33SEJGUMQCLQUXI","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d602d1d6a08d90a5ba1be7c073afc6fb279e022f94e4f33c5d1226e45b724876","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-09-30T20:06:43Z","title_canon_sha256":"ace6c3068c897b6c8855a1941775eff858eb72765619556c37b0d656f1f411fb"},"schema_version":"1.0","source":{"id":"2110.00075","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2110.00075","created_at":"2026-07-05T05:04:09Z"},{"alias_kind":"arxiv_version","alias_value":"2110.00075v2","created_at":"2026-07-05T05:04:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.00075","created_at":"2026-07-05T05:04:09Z"},{"alias_kind":"pith_short_12","alias_value":"I3PJWLKGUHV3","created_at":"2026-07-05T05:04:09Z"},{"alias_kind":"pith_short_16","alias_value":"I3PJWLKGUHV33SEJ","created_at":"2026-07-05T05:04:09Z"},{"alias_kind":"pith_short_8","alias_value":"I3PJWLKG","created_at":"2026-07-05T05:04:09Z"}],"graph_snapshots":[{"event_id":"sha256:cffefaf98bfa91d7eee03f0eb5f2637611db33b955a3416728ae68ecb364940e","target":"graph","created_at":"2026-07-05T05:04:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2110.00075/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose Noise2Recon, a model-agnostic, consistency training method for joint MRI reconstruction and denoising that can use both fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised and self-supervised settings. With limited or no labeled traini","authors_text":"Akshay S Chaudhari, Arjun D Desai, Batu M Ozturkler, Brian A Hargreaves, Christopher M R\\'e, Christopher M Sandino, John M Pauly, Marc Willis, Robert Boutin, Shreyas Vasanawala","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-09-30T20:06:43Z","title":"Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.00075","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:bb2d74c04d21316e89550e1cd0c6d4359b5de7935602ee68de586b690cb3920c","target":"record","created_at":"2026-07-05T05:04:09Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d602d1d6a08d90a5ba1be7c073afc6fb279e022f94e4f33c5d1226e45b724876","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-09-30T20:06:43Z","title_canon_sha256":"ace6c3068c897b6c8855a1941775eff858eb72765619556c37b0d656f1f411fb"},"schema_version":"1.0","source":{"id":"2110.00075","kind":"arxiv","version":2}},"canonical_sha256":"46de9b2d46a1ebbdc8893519012e14ba184d9c7a95f15a1969bf782e17916661","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"46de9b2d46a1ebbdc8893519012e14ba184d9c7a95f15a1969bf782e17916661","first_computed_at":"2026-07-05T05:04:09.840496Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:04:09.840496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j1gfJLM04z8iWrrqC3MvPCecezGa/JOdSrDJkVNgt0lE95j6Y5xrxBiFqOUR36COlIS0V5Gk9jNG7X9S3QNuCA==","signature_status":"signed_v1","signed_at":"2026-07-05T05:04:09.840920Z","signed_message":"canonical_sha256_bytes"},"source_id":"2110.00075","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bb2d74c04d21316e89550e1cd0c6d4359b5de7935602ee68de586b690cb3920c","sha256:cffefaf98bfa91d7eee03f0eb5f2637611db33b955a3416728ae68ecb364940e"],"state_sha256":"3c7bf5b570705c0e5134fd49eb3d4dc5677278c0b867526a1dd6d7a2d75a3011"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eAaIWLTCoVFxCpyqwGMq8cNPHNM1E564lh63okfJ3Qpx1Xk5yQ6ltCjtL9FqRun6VYJjK5WUdCTdhsIuDfr1AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T17:22:55.318366Z","bundle_sha256":"f77d6b968e1ec4fc5d8e5ac7bfd9ead61040ea76b3142b8136f7961711fdbb14"}}