{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:QGX5FOYMK5DBUQ2CKLM56Q4WKG","short_pith_number":"pith:QGX5FOYM","canonical_record":{"source":{"id":"1908.09262","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2019-08-25T07:07:35Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"878463c4f551e28df53b0225cae1a148536f55ed8bdd9056789dc968ed01a145","abstract_canon_sha256":"c63eaf4aeff03f2284dfddc1073c89673e1b14c18e0707740f52372323ac2ca2"},"schema_version":"1.0"},"canonical_sha256":"81afd2bb0c57461a434252d9df43965197d399218469912b835d2344686618b1","source":{"kind":"arxiv","id":"1908.09262","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.09262","created_at":"2026-07-04T23:59:38Z"},{"alias_kind":"arxiv_version","alias_value":"1908.09262v1","created_at":"2026-07-04T23:59:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.09262","created_at":"2026-07-04T23:59:38Z"},{"alias_kind":"pith_short_12","alias_value":"QGX5FOYMK5DB","created_at":"2026-07-04T23:59:38Z"},{"alias_kind":"pith_short_16","alias_value":"QGX5FOYMK5DBUQ2C","created_at":"2026-07-04T23:59:38Z"},{"alias_kind":"pith_short_8","alias_value":"QGX5FOYM","created_at":"2026-07-04T23:59:38Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:QGX5FOYMK5DBUQ2CKLM56Q4WKG","target":"record","payload":{"canonical_record":{"source":{"id":"1908.09262","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2019-08-25T07:07:35Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"878463c4f551e28df53b0225cae1a148536f55ed8bdd9056789dc968ed01a145","abstract_canon_sha256":"c63eaf4aeff03f2284dfddc1073c89673e1b14c18e0707740f52372323ac2ca2"},"schema_version":"1.0"},"canonical_sha256":"81afd2bb0c57461a434252d9df43965197d399218469912b835d2344686618b1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:59:38.628997Z","signature_b64":"atNDcdN1QwYoV8X8c2hgHO8vzUf+xg8nQO8q5CKP8LtkIX7IoLliLArVrW0uuXdGBuVZHnQqV3uC8Wxdz86VAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81afd2bb0c57461a434252d9df43965197d399218469912b835d2344686618b1","last_reissued_at":"2026-07-04T23:59:38.628589Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:59:38.628589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.09262","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T23:59:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zPT/cqhBIBh4SA1AgzDcqP5y4NrlGcFvZCWvoLj+lPsn6diDlYp+fVG3u9PZlWcZ/MZUj6z/D9St4TH/rEGnDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T13:23:06.787695Z"},"content_sha256":"44afea3a921376dd29ce296a36a47ad9ae516030ad757c8ed759d380a0f870f8","schema_version":"1.0","event_id":"sha256:44afea3a921376dd29ce296a36a47ad9ae516030ad757c8ed759d380a0f870f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:QGX5FOYMK5DBUQ2CKLM56Q4WKG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Balamurali Murugesan, Kaushik Sarveswaran, Keerthi Ram, Mohanasankar Sivaprakasam, Vijaya Raghavan S","submitted_at":"2019-08-25T07:07:35Z","abstract_excerpt":"Magnetic resonance imaging (MRI) is one of the best medical imaging modalities as it offers excellent spatial resolution and soft-tissue contrast. But, the usage of MRI is limited by its slow acquisition time, which makes it expensive and causes patient discomfort. In order to accelerate the acquisition, multiple deep learning networks have been proposed. Recently, Generative Adversarial Networks (GANs) have shown promising results in MRI reconstruction. The drawback with the proposed GAN based methods is it does not incorporate the prior information about the end goal which could help in bett"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.09262","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/1908.09262/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":"2026-07-04T23:59:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dfIidNxsK/ZNZ0VqRMNGV2LKBqU91iKqcLe0Nrd7b8yGkdgKRdA5+buur1hbmfCfvMPhLBd7yYcgkccdzZmICw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T13:23:06.788211Z"},"content_sha256":"09a96d591c8c58a92d8ed001fbd465915f77d71364fcc1348e21ee037882d987","schema_version":"1.0","event_id":"sha256:09a96d591c8c58a92d8ed001fbd465915f77d71364fcc1348e21ee037882d987"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QGX5FOYMK5DBUQ2CKLM56Q4WKG/bundle.json","state_url":"https://pith.science/pith/QGX5FOYMK5DBUQ2CKLM56Q4WKG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QGX5FOYMK5DBUQ2CKLM56Q4WKG/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":"2026-08-16T13:23:06Z","links":{"resolver":"https://pith.science/pith/QGX5FOYMK5DBUQ2CKLM56Q4WKG","bundle":"https://pith.science/pith/QGX5FOYMK5DBUQ2CKLM56Q4WKG/bundle.json","state":"https://pith.science/pith/QGX5FOYMK5DBUQ2CKLM56Q4WKG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QGX5FOYMK5DBUQ2CKLM56Q4WKG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:QGX5FOYMK5DBUQ2CKLM56Q4WKG","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":"c63eaf4aeff03f2284dfddc1073c89673e1b14c18e0707740f52372323ac2ca2","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2019-08-25T07:07:35Z","title_canon_sha256":"878463c4f551e28df53b0225cae1a148536f55ed8bdd9056789dc968ed01a145"},"schema_version":"1.0","source":{"id":"1908.09262","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.09262","created_at":"2026-07-04T23:59:38Z"},{"alias_kind":"arxiv_version","alias_value":"1908.09262v1","created_at":"2026-07-04T23:59:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.09262","created_at":"2026-07-04T23:59:38Z"},{"alias_kind":"pith_short_12","alias_value":"QGX5FOYMK5DB","created_at":"2026-07-04T23:59:38Z"},{"alias_kind":"pith_short_16","alias_value":"QGX5FOYMK5DBUQ2C","created_at":"2026-07-04T23:59:38Z"},{"alias_kind":"pith_short_8","alias_value":"QGX5FOYM","created_at":"2026-07-04T23:59:38Z"}],"graph_snapshots":[{"event_id":"sha256:09a96d591c8c58a92d8ed001fbd465915f77d71364fcc1348e21ee037882d987","target":"graph","created_at":"2026-07-04T23:59:38Z","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/1908.09262/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Magnetic resonance imaging (MRI) is one of the best medical imaging modalities as it offers excellent spatial resolution and soft-tissue contrast. But, the usage of MRI is limited by its slow acquisition time, which makes it expensive and causes patient discomfort. In order to accelerate the acquisition, multiple deep learning networks have been proposed. Recently, Generative Adversarial Networks (GANs) have shown promising results in MRI reconstruction. The drawback with the proposed GAN based methods is it does not incorporate the prior information about the end goal which could help in bett","authors_text":"Balamurali Murugesan, Kaushik Sarveswaran, Keerthi Ram, Mohanasankar Sivaprakasam, Vijaya Raghavan S","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2019-08-25T07:07:35Z","title":"Recon-GLGAN: A Global-Local context based Generative Adversarial Network for MRI Reconstruction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.09262","kind":"arxiv","version":1},"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":"sha256:44afea3a921376dd29ce296a36a47ad9ae516030ad757c8ed759d380a0f870f8","target":"record","created_at":"2026-07-04T23:59:38Z","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":"c63eaf4aeff03f2284dfddc1073c89673e1b14c18e0707740f52372323ac2ca2","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2019-08-25T07:07:35Z","title_canon_sha256":"878463c4f551e28df53b0225cae1a148536f55ed8bdd9056789dc968ed01a145"},"schema_version":"1.0","source":{"id":"1908.09262","kind":"arxiv","version":1}},"canonical_sha256":"81afd2bb0c57461a434252d9df43965197d399218469912b835d2344686618b1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"81afd2bb0c57461a434252d9df43965197d399218469912b835d2344686618b1","first_computed_at":"2026-07-04T23:59:38.628589Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:59:38.628589Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"atNDcdN1QwYoV8X8c2hgHO8vzUf+xg8nQO8q5CKP8LtkIX7IoLliLArVrW0uuXdGBuVZHnQqV3uC8Wxdz86VAw==","signature_status":"signed_v1","signed_at":"2026-07-04T23:59:38.628997Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.09262","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:44afea3a921376dd29ce296a36a47ad9ae516030ad757c8ed759d380a0f870f8","sha256:09a96d591c8c58a92d8ed001fbd465915f77d71364fcc1348e21ee037882d987"],"state_sha256":"118e3e0964f346a5a91ff9162491ca8cbf589f22665bd40d3ca32e6b1d231998"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A0V+lUPdwMWwrPxjO9KT2VvjQBjeOeoxThOv4MWbo6o5ZEKJk3WPkSch44xvtwHL0WmvWA2hOeLbgC7eJXoYCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T13:23:06.793650Z","bundle_sha256":"51287bbaee83b76b8191643af23d8e9dbd862caf57458b271e1919e1d87d9250"}}