{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:2GCUSD2CHGRUQK6LQSQ4K5N5KS","short_pith_number":"pith:2GCUSD2C","schema_version":"1.0","canonical_sha256":"d185490f4239a3482bcb84a1c575bd54ba5731504053013af2ac4c6e6d6c7741","source":{"kind":"arxiv","id":"1807.01224","version":2},"attestation_state":"computed","paper":{"title":"Deep neural networks for non-linear model-based ultrasound reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"eess.IV","authors_text":"Charles A. Bouman, Gregery T. Buzzard, Hani Almansouri, Hector Santos-Villalobos, S.V. Venkatakrishnan","submitted_at":"2018-07-03T15:01:05Z","abstract_excerpt":"Ultrasound reflection tomography is widely used to image large complex specimens that are only accessible from a single side, such as well systems and nuclear power plant containment walls. Typical methods for inverting the measurement rely on delay-and-sum algorithms that rapidly produce reconstructions but with significant artifacts. Recently, model-based reconstruction approaches using a linear forward model have been shown to significantly improve image quality compared to the conventional approach. However, even these techniques result in artifacts for complex objects because of the inher"},"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":"1807.01224","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-07-03T15:01:05Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"cd4bbf669eef5b6327648f8f4d89e53b97a22b263f30143b284f77cc665889b5","abstract_canon_sha256":"c729917f0fd14a78c834c41f35bd0dab7c06bd9424464b1ef7c0df059d3cc88a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:33.973600Z","signature_b64":"R7+n4xhPtVhSpsrq1YBBTiMmA/SIOsYIeIKdz2I6RwFN5wKYmErECMpj2TFIsoRDOj0XUr7flkX/K9A32RjWBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d185490f4239a3482bcb84a1c575bd54ba5731504053013af2ac4c6e6d6c7741","last_reissued_at":"2026-05-18T00:04:33.973099Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:33.973099Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep neural networks for non-linear model-based ultrasound reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"eess.IV","authors_text":"Charles A. Bouman, Gregery T. Buzzard, Hani Almansouri, Hector Santos-Villalobos, S.V. Venkatakrishnan","submitted_at":"2018-07-03T15:01:05Z","abstract_excerpt":"Ultrasound reflection tomography is widely used to image large complex specimens that are only accessible from a single side, such as well systems and nuclear power plant containment walls. Typical methods for inverting the measurement rely on delay-and-sum algorithms that rapidly produce reconstructions but with significant artifacts. Recently, model-based reconstruction approaches using a linear forward model have been shown to significantly improve image quality compared to the conventional approach. However, even these techniques result in artifacts for complex objects because of the inher"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.01224","kind":"arxiv","version":2},"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":"1807.01224","created_at":"2026-05-18T00:04:33.973177+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.01224v2","created_at":"2026-05-18T00:04:33.973177+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.01224","created_at":"2026-05-18T00:04:33.973177+00:00"},{"alias_kind":"pith_short_12","alias_value":"2GCUSD2CHGRU","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"2GCUSD2CHGRUQK6L","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"2GCUSD2C","created_at":"2026-05-18T12:32:02.567920+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/2GCUSD2CHGRUQK6LQSQ4K5N5KS","json":"https://pith.science/pith/2GCUSD2CHGRUQK6LQSQ4K5N5KS.json","graph_json":"https://pith.science/api/pith-number/2GCUSD2CHGRUQK6LQSQ4K5N5KS/graph.json","events_json":"https://pith.science/api/pith-number/2GCUSD2CHGRUQK6LQSQ4K5N5KS/events.json","paper":"https://pith.science/paper/2GCUSD2C"},"agent_actions":{"view_html":"https://pith.science/pith/2GCUSD2CHGRUQK6LQSQ4K5N5KS","download_json":"https://pith.science/pith/2GCUSD2CHGRUQK6LQSQ4K5N5KS.json","view_paper":"https://pith.science/paper/2GCUSD2C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.01224&json=true","fetch_graph":"https://pith.science/api/pith-number/2GCUSD2CHGRUQK6LQSQ4K5N5KS/graph.json","fetch_events":"https://pith.science/api/pith-number/2GCUSD2CHGRUQK6LQSQ4K5N5KS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2GCUSD2CHGRUQK6LQSQ4K5N5KS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2GCUSD2CHGRUQK6LQSQ4K5N5KS/action/storage_attestation","attest_author":"https://pith.science/pith/2GCUSD2CHGRUQK6LQSQ4K5N5KS/action/author_attestation","sign_citation":"https://pith.science/pith/2GCUSD2CHGRUQK6LQSQ4K5N5KS/action/citation_signature","submit_replication":"https://pith.science/pith/2GCUSD2CHGRUQK6LQSQ4K5N5KS/action/replication_record"}},"created_at":"2026-05-18T00:04:33.973177+00:00","updated_at":"2026-05-18T00:04:33.973177+00:00"}