{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:XLH2PQLNJVVDEXSCRBAASHU7ND","short_pith_number":"pith:XLH2PQLN","schema_version":"1.0","canonical_sha256":"bacfa7c16d4d6a325e428840091e9f68ce66c3796cb34412e947752784d476a6","source":{"kind":"arxiv","id":"2408.11754","version":1},"attestation_state":"computed","paper":{"title":"Improving the Scan-rescan Precision of AI-based CMR Biomarker Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.IV"],"primary_cat":"q-bio.QM","authors_text":"Andrew P. King, Dewmini Hasara Wickremasinghe, Esther Puyol-Ant\\'on, Paul Aljabar, Reza Razavi, Yiyang Xu","submitted_at":"2024-08-21T16:24:27Z","abstract_excerpt":"Quantification of cardiac biomarkers from cine cardiovascular magnetic resonance (CMR) data using deep learning (DL) methods offers many advantages, such as increased accuracy and faster analysis. However, only a few studies have focused on the scan-rescan precision of the biomarker estimates, which is important for reproducibility and longitudinal analysis. Here, we propose a cardiac biomarker estimation pipeline that not only focuses on achieving high segmentation accuracy but also on improving the scan-rescan precision of the computed biomarkers, namely left and right ventricular ejection f"},"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":"2408.11754","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2024-08-21T16:24:27Z","cross_cats_sorted":["cs.AI","eess.IV"],"title_canon_sha256":"8dd37b476c4ad0d1cd9248bc2b835874cf8c9c6d8337f3673a77829e92a40438","abstract_canon_sha256":"b242963d80eb89857e9a66eb8aaecae0372d353b77c8798523fb152e9dede8a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:48.607114Z","signature_b64":"ChvU9lYVn3dhC4q362GJqjkrA1sp6Vr9N6dyyI5aMxJjyN2TO2teKRUKZS7smi7lrDXPvw7Uvj8EzmPQHmWTDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bacfa7c16d4d6a325e428840091e9f68ce66c3796cb34412e947752784d476a6","last_reissued_at":"2026-07-05T08:57:48.606656Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:48.606656Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving the Scan-rescan Precision of AI-based CMR Biomarker Estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","eess.IV"],"primary_cat":"q-bio.QM","authors_text":"Andrew P. King, Dewmini Hasara Wickremasinghe, Esther Puyol-Ant\\'on, Paul Aljabar, Reza Razavi, Yiyang Xu","submitted_at":"2024-08-21T16:24:27Z","abstract_excerpt":"Quantification of cardiac biomarkers from cine cardiovascular magnetic resonance (CMR) data using deep learning (DL) methods offers many advantages, such as increased accuracy and faster analysis. However, only a few studies have focused on the scan-rescan precision of the biomarker estimates, which is important for reproducibility and longitudinal analysis. Here, we propose a cardiac biomarker estimation pipeline that not only focuses on achieving high segmentation accuracy but also on improving the scan-rescan precision of the computed biomarkers, namely left and right ventricular ejection f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.11754","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/2408.11754/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":"2408.11754","created_at":"2026-07-05T08:57:48.606717+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.11754v1","created_at":"2026-07-05T08:57:48.606717+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.11754","created_at":"2026-07-05T08:57:48.606717+00:00"},{"alias_kind":"pith_short_12","alias_value":"XLH2PQLNJVVD","created_at":"2026-07-05T08:57:48.606717+00:00"},{"alias_kind":"pith_short_16","alias_value":"XLH2PQLNJVVDEXSC","created_at":"2026-07-05T08:57:48.606717+00:00"},{"alias_kind":"pith_short_8","alias_value":"XLH2PQLN","created_at":"2026-07-05T08:57:48.606717+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/XLH2PQLNJVVDEXSCRBAASHU7ND","json":"https://pith.science/pith/XLH2PQLNJVVDEXSCRBAASHU7ND.json","graph_json":"https://pith.science/api/pith-number/XLH2PQLNJVVDEXSCRBAASHU7ND/graph.json","events_json":"https://pith.science/api/pith-number/XLH2PQLNJVVDEXSCRBAASHU7ND/events.json","paper":"https://pith.science/paper/XLH2PQLN"},"agent_actions":{"view_html":"https://pith.science/pith/XLH2PQLNJVVDEXSCRBAASHU7ND","download_json":"https://pith.science/pith/XLH2PQLNJVVDEXSCRBAASHU7ND.json","view_paper":"https://pith.science/paper/XLH2PQLN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.11754&json=true","fetch_graph":"https://pith.science/api/pith-number/XLH2PQLNJVVDEXSCRBAASHU7ND/graph.json","fetch_events":"https://pith.science/api/pith-number/XLH2PQLNJVVDEXSCRBAASHU7ND/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XLH2PQLNJVVDEXSCRBAASHU7ND/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XLH2PQLNJVVDEXSCRBAASHU7ND/action/storage_attestation","attest_author":"https://pith.science/pith/XLH2PQLNJVVDEXSCRBAASHU7ND/action/author_attestation","sign_citation":"https://pith.science/pith/XLH2PQLNJVVDEXSCRBAASHU7ND/action/citation_signature","submit_replication":"https://pith.science/pith/XLH2PQLNJVVDEXSCRBAASHU7ND/action/replication_record"}},"created_at":"2026-07-05T08:57:48.606717+00:00","updated_at":"2026-07-05T08:57:48.606717+00:00"}