{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:Q4Q6MJXU6UYYJIHHA6YNC5IIEX","short_pith_number":"pith:Q4Q6MJXU","schema_version":"1.0","canonical_sha256":"8721e626f4f53184a0e707b0d1750825d7508f76fdda5b96d4008b213d232853","source":{"kind":"arxiv","id":"2502.01057","version":3},"attestation_state":"computed","paper":{"title":"FetDTIAlign: A Deep Learning Framework for Affine and Deformable Registration of Fetal Brain dMRI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"eess.IV","authors_text":"Bo Li, Davood Karimi, Qi Zeng, Simon K. Warfield","submitted_at":"2025-02-03T05:10:00Z","abstract_excerpt":"Diffusion MRI (dMRI) provides unique insights into fetal brain microstructure in utero. Longitudinal and cross-sectional fetal dMRI studies can reveal crucial neurodevelopmental changes but require precise spatial alignment across scans and subjects. This is challenging due to low data quality, rapid brain development, and limited anatomical landmarks. Existing registration methods, designed for high-quality adult data, struggle with these complexities. To address this, we introduce FetDTIAlign, a deep learning approach for fetal brain dMRI registration, enabling accurate affine and deformable"},"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":"2502.01057","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-02-03T05:10:00Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"810a331e733952747cf2652c4e6d948e8945ff0f6f838f608d907c58c9cb62ee","abstract_canon_sha256":"eeef5d242e748fe9274f175acba410cd7377fb3849bb2cb51f2219e50e9a60ee"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:31.556650Z","signature_b64":"hF/6KJBlF37rqPt7v2/DZYghpaieZeqsrC8rbjylSSLDTJrTerCHdqVP+HZk9uH1xbEAm4gUxlmOV2GPRk4FAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8721e626f4f53184a0e707b0d1750825d7508f76fdda5b96d4008b213d232853","last_reissued_at":"2026-07-05T11:01:31.556138Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:31.556138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FetDTIAlign: A Deep Learning Framework for Affine and Deformable Registration of Fetal Brain dMRI","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"eess.IV","authors_text":"Bo Li, Davood Karimi, Qi Zeng, Simon K. Warfield","submitted_at":"2025-02-03T05:10:00Z","abstract_excerpt":"Diffusion MRI (dMRI) provides unique insights into fetal brain microstructure in utero. Longitudinal and cross-sectional fetal dMRI studies can reveal crucial neurodevelopmental changes but require precise spatial alignment across scans and subjects. This is challenging due to low data quality, rapid brain development, and limited anatomical landmarks. Existing registration methods, designed for high-quality adult data, struggle with these complexities. To address this, we introduce FetDTIAlign, a deep learning approach for fetal brain dMRI registration, enabling accurate affine and deformable"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01057","kind":"arxiv","version":3},"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/2502.01057/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":"2502.01057","created_at":"2026-07-05T11:01:31.556192+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.01057v3","created_at":"2026-07-05T11:01:31.556192+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01057","created_at":"2026-07-05T11:01:31.556192+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q4Q6MJXU6UYY","created_at":"2026-07-05T11:01:31.556192+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q4Q6MJXU6UYYJIHH","created_at":"2026-07-05T11:01:31.556192+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q4Q6MJXU","created_at":"2026-07-05T11:01:31.556192+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/Q4Q6MJXU6UYYJIHHA6YNC5IIEX","json":"https://pith.science/pith/Q4Q6MJXU6UYYJIHHA6YNC5IIEX.json","graph_json":"https://pith.science/api/pith-number/Q4Q6MJXU6UYYJIHHA6YNC5IIEX/graph.json","events_json":"https://pith.science/api/pith-number/Q4Q6MJXU6UYYJIHHA6YNC5IIEX/events.json","paper":"https://pith.science/paper/Q4Q6MJXU"},"agent_actions":{"view_html":"https://pith.science/pith/Q4Q6MJXU6UYYJIHHA6YNC5IIEX","download_json":"https://pith.science/pith/Q4Q6MJXU6UYYJIHHA6YNC5IIEX.json","view_paper":"https://pith.science/paper/Q4Q6MJXU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.01057&json=true","fetch_graph":"https://pith.science/api/pith-number/Q4Q6MJXU6UYYJIHHA6YNC5IIEX/graph.json","fetch_events":"https://pith.science/api/pith-number/Q4Q6MJXU6UYYJIHHA6YNC5IIEX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q4Q6MJXU6UYYJIHHA6YNC5IIEX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q4Q6MJXU6UYYJIHHA6YNC5IIEX/action/storage_attestation","attest_author":"https://pith.science/pith/Q4Q6MJXU6UYYJIHHA6YNC5IIEX/action/author_attestation","sign_citation":"https://pith.science/pith/Q4Q6MJXU6UYYJIHHA6YNC5IIEX/action/citation_signature","submit_replication":"https://pith.science/pith/Q4Q6MJXU6UYYJIHHA6YNC5IIEX/action/replication_record"}},"created_at":"2026-07-05T11:01:31.556192+00:00","updated_at":"2026-07-05T11:01:31.556192+00:00"}