{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:HUHGGYJUTTVTZYY3IBJOKFPK2G","short_pith_number":"pith:HUHGGYJU","schema_version":"1.0","canonical_sha256":"3d0e6361349ceb3ce31b4052e515ead19125278d64f5c225392a5286befdf3b8","source":{"kind":"arxiv","id":"1907.04500","version":1},"attestation_state":"computed","paper":{"title":"Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Elfar Adalsteinsson, Ellen Grant, Esra Abaci Turk, Junshen Xu, Kui Ying, Larry Zhang, Molin Zhang, Polina Golland","submitted_at":"2019-07-10T03:56:02Z","abstract_excerpt":"The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable and rapid on the scale of conventional imaging times, limits the set of viable acquisition techniques to single-shot imaging with severe compromises in signal-to-noise ratio and diagnostic contrast, and frequently results in unacceptable image quality. Surprisingly little is known about the characteristics of fetal motion during MRI and here we propose and demonstrate methods that exploit a growing repository of MRI ob"},"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":"1907.04500","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-07-10T03:56:02Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"168d6ee3c401377c2234b213d6ae5889e5b3c729c2a583833b87cbbd61b7b33c","abstract_canon_sha256":"078adf203037915df4a7b9937cb31dddf46894c3e82164f9da1086992d12164e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:00.058372Z","signature_b64":"CaRNhP6D3fH+60Wb45i6Afv2TvfngS4JWOA9ZiQEdQnLD2plWfnwiBGcsiP8fqgctHOX31RRps7YcsbVUL18Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d0e6361349ceb3ce31b4052e515ead19125278d64f5c225392a5286befdf3b8","last_reissued_at":"2026-05-17T23:41:00.057760Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:00.057760Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Elfar Adalsteinsson, Ellen Grant, Esra Abaci Turk, Junshen Xu, Kui Ying, Larry Zhang, Molin Zhang, Polina Golland","submitted_at":"2019-07-10T03:56:02Z","abstract_excerpt":"The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable and rapid on the scale of conventional imaging times, limits the set of viable acquisition techniques to single-shot imaging with severe compromises in signal-to-noise ratio and diagnostic contrast, and frequently results in unacceptable image quality. Surprisingly little is known about the characteristics of fetal motion during MRI and here we propose and demonstrate methods that exploit a growing repository of MRI ob"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.04500","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":"1907.04500","created_at":"2026-05-17T23:41:00.057849+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.04500v1","created_at":"2026-05-17T23:41:00.057849+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.04500","created_at":"2026-05-17T23:41:00.057849+00:00"},{"alias_kind":"pith_short_12","alias_value":"HUHGGYJUTTVT","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"HUHGGYJUTTVTZYY3","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"HUHGGYJU","created_at":"2026-05-18T12:33:18.533446+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/HUHGGYJUTTVTZYY3IBJOKFPK2G","json":"https://pith.science/pith/HUHGGYJUTTVTZYY3IBJOKFPK2G.json","graph_json":"https://pith.science/api/pith-number/HUHGGYJUTTVTZYY3IBJOKFPK2G/graph.json","events_json":"https://pith.science/api/pith-number/HUHGGYJUTTVTZYY3IBJOKFPK2G/events.json","paper":"https://pith.science/paper/HUHGGYJU"},"agent_actions":{"view_html":"https://pith.science/pith/HUHGGYJUTTVTZYY3IBJOKFPK2G","download_json":"https://pith.science/pith/HUHGGYJUTTVTZYY3IBJOKFPK2G.json","view_paper":"https://pith.science/paper/HUHGGYJU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.04500&json=true","fetch_graph":"https://pith.science/api/pith-number/HUHGGYJUTTVTZYY3IBJOKFPK2G/graph.json","fetch_events":"https://pith.science/api/pith-number/HUHGGYJUTTVTZYY3IBJOKFPK2G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HUHGGYJUTTVTZYY3IBJOKFPK2G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HUHGGYJUTTVTZYY3IBJOKFPK2G/action/storage_attestation","attest_author":"https://pith.science/pith/HUHGGYJUTTVTZYY3IBJOKFPK2G/action/author_attestation","sign_citation":"https://pith.science/pith/HUHGGYJUTTVTZYY3IBJOKFPK2G/action/citation_signature","submit_replication":"https://pith.science/pith/HUHGGYJUTTVTZYY3IBJOKFPK2G/action/replication_record"}},"created_at":"2026-05-17T23:41:00.057849+00:00","updated_at":"2026-05-17T23:41:00.057849+00:00"}