{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:GJV4KQPN4DPL36ANNKT2QKZJ6V","short_pith_number":"pith:GJV4KQPN","schema_version":"1.0","canonical_sha256":"326bc541ede0debdf80d6aa7a82b29f57bc6c126c85333a32a3ae5fea0b3f7ec","source":{"kind":"arxiv","id":"2309.14371","version":1},"attestation_state":"computed","paper":{"title":"Deep learning based workflow for accelerated industrial X-ray Computed Tomography","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Amirkoushyar Ziabari, Curtis Frederick, Luke Scime, Obaidullah Rahman, Paul Brackman, Ryan Dehoff, Singanallur V. Venkatakrishnan, Vincent Paquit","submitted_at":"2023-09-24T00:43:34Z","abstract_excerpt":"X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal components may have beam hardening artifacts such as cupping and streaking which makes reliable detection of flaws and defects challenging. Furthermore, traditional workflows based on using analytic reconstruction algorithms require a large number of projections for accurate characterization - leading to longer measurement times and hindering the adoption of XCT for in-line inspections. In this paper, we introduce a "},"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":"2309.14371","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-09-24T00:43:34Z","cross_cats_sorted":[],"title_canon_sha256":"72e66af67f7546e373b142a0aab23cf9785fe601240148d1c2f61c0aa42ecfce","abstract_canon_sha256":"b263f5466490ebe1b20d40dbb5102b15a53788b3388b00d09820310ee40080ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:54:24.568481Z","signature_b64":"Ol8niEgys9YsKF7DOj9fyEpzKxMjTTBEuKBeMwpHaksK8pS6cogDXCfB6+sVWy3VybEmYozA8hEabTkW2i4kDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"326bc541ede0debdf80d6aa7a82b29f57bc6c126c85333a32a3ae5fea0b3f7ec","last_reissued_at":"2026-07-05T06:54:24.567992Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:54:24.567992Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep learning based workflow for accelerated industrial X-ray Computed Tomography","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Amirkoushyar Ziabari, Curtis Frederick, Luke Scime, Obaidullah Rahman, Paul Brackman, Ryan Dehoff, Singanallur V. Venkatakrishnan, Vincent Paquit","submitted_at":"2023-09-24T00:43:34Z","abstract_excerpt":"X-ray computed tomography (XCT) is an important tool for high-resolution non-destructive characterization of additively-manufactured metal components. XCT reconstructions of metal components may have beam hardening artifacts such as cupping and streaking which makes reliable detection of flaws and defects challenging. Furthermore, traditional workflows based on using analytic reconstruction algorithms require a large number of projections for accurate characterization - leading to longer measurement times and hindering the adoption of XCT for in-line inspections. In this paper, we introduce a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.14371","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/2309.14371/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":"2309.14371","created_at":"2026-07-05T06:54:24.568053+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.14371v1","created_at":"2026-07-05T06:54:24.568053+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.14371","created_at":"2026-07-05T06:54:24.568053+00:00"},{"alias_kind":"pith_short_12","alias_value":"GJV4KQPN4DPL","created_at":"2026-07-05T06:54:24.568053+00:00"},{"alias_kind":"pith_short_16","alias_value":"GJV4KQPN4DPL36AN","created_at":"2026-07-05T06:54:24.568053+00:00"},{"alias_kind":"pith_short_8","alias_value":"GJV4KQPN","created_at":"2026-07-05T06:54:24.568053+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/GJV4KQPN4DPL36ANNKT2QKZJ6V","json":"https://pith.science/pith/GJV4KQPN4DPL36ANNKT2QKZJ6V.json","graph_json":"https://pith.science/api/pith-number/GJV4KQPN4DPL36ANNKT2QKZJ6V/graph.json","events_json":"https://pith.science/api/pith-number/GJV4KQPN4DPL36ANNKT2QKZJ6V/events.json","paper":"https://pith.science/paper/GJV4KQPN"},"agent_actions":{"view_html":"https://pith.science/pith/GJV4KQPN4DPL36ANNKT2QKZJ6V","download_json":"https://pith.science/pith/GJV4KQPN4DPL36ANNKT2QKZJ6V.json","view_paper":"https://pith.science/paper/GJV4KQPN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.14371&json=true","fetch_graph":"https://pith.science/api/pith-number/GJV4KQPN4DPL36ANNKT2QKZJ6V/graph.json","fetch_events":"https://pith.science/api/pith-number/GJV4KQPN4DPL36ANNKT2QKZJ6V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GJV4KQPN4DPL36ANNKT2QKZJ6V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GJV4KQPN4DPL36ANNKT2QKZJ6V/action/storage_attestation","attest_author":"https://pith.science/pith/GJV4KQPN4DPL36ANNKT2QKZJ6V/action/author_attestation","sign_citation":"https://pith.science/pith/GJV4KQPN4DPL36ANNKT2QKZJ6V/action/citation_signature","submit_replication":"https://pith.science/pith/GJV4KQPN4DPL36ANNKT2QKZJ6V/action/replication_record"}},"created_at":"2026-07-05T06:54:24.568053+00:00","updated_at":"2026-07-05T06:54:24.568053+00:00"}