{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EANLXV6C5BYGGAITSFRD37DK2P","short_pith_number":"pith:EANLXV6C","schema_version":"1.0","canonical_sha256":"201abbd7c2e87063011391623dfc6ad3e8e6e35798fd09d722e62e4ba30e3bf0","source":{"kind":"arxiv","id":"2312.15575","version":1},"attestation_state":"computed","paper":{"title":"Neural Born Series Operator for Biomedical Ultrasound Computed Tomography","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"He Sun, Yihang Zheng, Youjia Zheng, Yubing Li, Zhijun Zeng, Zuoqiang Shi","submitted_at":"2023-12-25T01:06:31Z","abstract_excerpt":"Ultrasound Computed Tomography (USCT) provides a radiation-free option for high-resolution clinical imaging. Despite its potential, the computationally intensive Full Waveform Inversion (FWI) required for tissue property reconstruction limits its clinical utility. This paper introduces the Neural Born Series Operator (NBSO), a novel technique designed to speed up wave simulations, thereby facilitating a more efficient USCT image reconstruction process through an NBSO-based FWI pipeline. Thoroughly validated on comprehensive brain and breast datasets, simulated under experimental USCT condition"},"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":"2312.15575","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-12-25T01:06:31Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"d6cf81db13d9bfb2168c3d337ba100865ce0bf386dd5c9654c95a43041fc03ed","abstract_canon_sha256":"97e3132962d76e92cd6613e74e84912b3150be7b5ade3cc8e5d0e4a7f50abdf8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:27:58.258200Z","signature_b64":"kKap1ioFl8LXNWdCeuR3cZ/HJyyPA8xmAEW/PATxwpUSuY0z1PlhMvB7F3Xd8geZsekFHCFgOcw85bfNW6gcCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"201abbd7c2e87063011391623dfc6ad3e8e6e35798fd09d722e62e4ba30e3bf0","last_reissued_at":"2026-07-05T07:27:58.257741Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:27:58.257741Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Born Series Operator for Biomedical Ultrasound Computed Tomography","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"He Sun, Yihang Zheng, Youjia Zheng, Yubing Li, Zhijun Zeng, Zuoqiang Shi","submitted_at":"2023-12-25T01:06:31Z","abstract_excerpt":"Ultrasound Computed Tomography (USCT) provides a radiation-free option for high-resolution clinical imaging. Despite its potential, the computationally intensive Full Waveform Inversion (FWI) required for tissue property reconstruction limits its clinical utility. This paper introduces the Neural Born Series Operator (NBSO), a novel technique designed to speed up wave simulations, thereby facilitating a more efficient USCT image reconstruction process through an NBSO-based FWI pipeline. Thoroughly validated on comprehensive brain and breast datasets, simulated under experimental USCT condition"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.15575","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/2312.15575/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":"2312.15575","created_at":"2026-07-05T07:27:58.257791+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.15575v1","created_at":"2026-07-05T07:27:58.257791+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.15575","created_at":"2026-07-05T07:27:58.257791+00:00"},{"alias_kind":"pith_short_12","alias_value":"EANLXV6C5BYG","created_at":"2026-07-05T07:27:58.257791+00:00"},{"alias_kind":"pith_short_16","alias_value":"EANLXV6C5BYGGAIT","created_at":"2026-07-05T07:27:58.257791+00:00"},{"alias_kind":"pith_short_8","alias_value":"EANLXV6C","created_at":"2026-07-05T07:27:58.257791+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2606.18305","citing_title":"Starter-Iterator Neural Operator: A Unified Architecture for High-Fidelity Forward and Inverse PDE Problems","ref_index":73,"is_internal_anchor":true},{"citing_arxiv_id":"2212.04466","citing_title":"A Full-waveform Approximation of Finite-Sized Acoustic Apertures: Forward and Adjoint Wavefields","ref_index":47,"is_internal_anchor":true},{"citing_arxiv_id":"2507.16344","citing_title":"Diff-ANO: Towards Fast High-Resolution Ultrasound Computed Tomography via Conditional Consistency Models and Adjoint Neural Operators","ref_index":57,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EANLXV6C5BYGGAITSFRD37DK2P","json":"https://pith.science/pith/EANLXV6C5BYGGAITSFRD37DK2P.json","graph_json":"https://pith.science/api/pith-number/EANLXV6C5BYGGAITSFRD37DK2P/graph.json","events_json":"https://pith.science/api/pith-number/EANLXV6C5BYGGAITSFRD37DK2P/events.json","paper":"https://pith.science/paper/EANLXV6C"},"agent_actions":{"view_html":"https://pith.science/pith/EANLXV6C5BYGGAITSFRD37DK2P","download_json":"https://pith.science/pith/EANLXV6C5BYGGAITSFRD37DK2P.json","view_paper":"https://pith.science/paper/EANLXV6C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.15575&json=true","fetch_graph":"https://pith.science/api/pith-number/EANLXV6C5BYGGAITSFRD37DK2P/graph.json","fetch_events":"https://pith.science/api/pith-number/EANLXV6C5BYGGAITSFRD37DK2P/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EANLXV6C5BYGGAITSFRD37DK2P/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EANLXV6C5BYGGAITSFRD37DK2P/action/storage_attestation","attest_author":"https://pith.science/pith/EANLXV6C5BYGGAITSFRD37DK2P/action/author_attestation","sign_citation":"https://pith.science/pith/EANLXV6C5BYGGAITSFRD37DK2P/action/citation_signature","submit_replication":"https://pith.science/pith/EANLXV6C5BYGGAITSFRD37DK2P/action/replication_record"}},"created_at":"2026-07-05T07:27:58.257791+00:00","updated_at":"2026-07-05T07:27:58.257791+00:00"}