{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CQGGERUQAQ7A6BRYGVAR2DMSMB","short_pith_number":"pith:CQGGERUQ","schema_version":"1.0","canonical_sha256":"140c624690043e0f063835411d0d92605b951622985802786b5c4c96ed247d37","source":{"kind":"arxiv","id":"1906.09745","version":1},"attestation_state":"computed","paper":{"title":"Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"eess.IV","authors_text":"Hing-Chiu Chang, Ka-Wai Kwok, Kit-Hang Lee, Qi Dou, Shihui Chen, Wenhao Jiang, Yui-Lun Ng, Zhiyu Liu","submitted_at":"2019-06-24T06:54:35Z","abstract_excerpt":"Abdominal magnetic resonance imaging (MRI) provides a straightforward way of characterizing tissue and locating lesions of patients as in standard diagnosis. However, abdominal MRI often suffers from respiratory motion artifacts, which leads to blurring and ghosting that significantly deteriorate the imaging quality. Conventional methods to reduce or eliminate these motion artifacts include breath holding, patient sedation, respiratory gating, and image post-processing, but these strategies inevitably involve extra scanning time and patient discomfort. In this paper, we propose a novel deep-le"},"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":"1906.09745","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-24T06:54:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7e6efd76d191a12fb37c179cac25482ed7c27ec71801fd3a938fbad36dc1a988","abstract_canon_sha256":"47d650c3c7b2e8571ab920d4ed74576ee386c21364f5b89077d5cd17561cbe5b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:36.889200Z","signature_b64":"Ypj2stbzgWlAD6annAb9IPlZPulIzflRVPl3NK8vYr5GveVibsGswItY602mD/JQZ/gJurR/nXV/6rUtht+/Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"140c624690043e0f063835411d0d92605b951622985802786b5c4c96ed247d37","last_reissued_at":"2026-05-17T23:42:36.888534Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:36.888534Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"eess.IV","authors_text":"Hing-Chiu Chang, Ka-Wai Kwok, Kit-Hang Lee, Qi Dou, Shihui Chen, Wenhao Jiang, Yui-Lun Ng, Zhiyu Liu","submitted_at":"2019-06-24T06:54:35Z","abstract_excerpt":"Abdominal magnetic resonance imaging (MRI) provides a straightforward way of characterizing tissue and locating lesions of patients as in standard diagnosis. However, abdominal MRI often suffers from respiratory motion artifacts, which leads to blurring and ghosting that significantly deteriorate the imaging quality. Conventional methods to reduce or eliminate these motion artifacts include breath holding, patient sedation, respiratory gating, and image post-processing, but these strategies inevitably involve extra scanning time and patient discomfort. In this paper, we propose a novel deep-le"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.09745","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":""},"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":"1906.09745","created_at":"2026-05-17T23:42:36.888630+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.09745v1","created_at":"2026-05-17T23:42:36.888630+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.09745","created_at":"2026-05-17T23:42:36.888630+00:00"},{"alias_kind":"pith_short_12","alias_value":"CQGGERUQAQ7A","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"CQGGERUQAQ7A6BRY","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"CQGGERUQ","created_at":"2026-05-18T12:33:15.570797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.10439","citing_title":"Removing Motion Artifact in MRI by Using a Perceptual Loss Driven Deep Learning Framework","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CQGGERUQAQ7A6BRYGVAR2DMSMB","json":"https://pith.science/pith/CQGGERUQAQ7A6BRYGVAR2DMSMB.json","graph_json":"https://pith.science/api/pith-number/CQGGERUQAQ7A6BRYGVAR2DMSMB/graph.json","events_json":"https://pith.science/api/pith-number/CQGGERUQAQ7A6BRYGVAR2DMSMB/events.json","paper":"https://pith.science/paper/CQGGERUQ"},"agent_actions":{"view_html":"https://pith.science/pith/CQGGERUQAQ7A6BRYGVAR2DMSMB","download_json":"https://pith.science/pith/CQGGERUQAQ7A6BRYGVAR2DMSMB.json","view_paper":"https://pith.science/paper/CQGGERUQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.09745&json=true","fetch_graph":"https://pith.science/api/pith-number/CQGGERUQAQ7A6BRYGVAR2DMSMB/graph.json","fetch_events":"https://pith.science/api/pith-number/CQGGERUQAQ7A6BRYGVAR2DMSMB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CQGGERUQAQ7A6BRYGVAR2DMSMB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CQGGERUQAQ7A6BRYGVAR2DMSMB/action/storage_attestation","attest_author":"https://pith.science/pith/CQGGERUQAQ7A6BRYGVAR2DMSMB/action/author_attestation","sign_citation":"https://pith.science/pith/CQGGERUQAQ7A6BRYGVAR2DMSMB/action/citation_signature","submit_replication":"https://pith.science/pith/CQGGERUQAQ7A6BRYGVAR2DMSMB/action/replication_record"}},"created_at":"2026-05-17T23:42:36.888630+00:00","updated_at":"2026-05-17T23:42:36.888630+00:00"}