{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DRDF7ARMCCK6NMIZCEGQ6A7USE","short_pith_number":"pith:DRDF7ARM","schema_version":"1.0","canonical_sha256":"1c465f822c1095e6b119110d0f03f4910e4e5605e1c3521f0b472992d8e6ab96","source":{"kind":"arxiv","id":"2406.18063","version":1},"attestation_state":"computed","paper":{"title":"Data-driven imaging geometric recovery of ultrahigh resolution robotic micro-CT for in-vivo and other applications","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"physics.med-ph","authors_text":"Ge Wang, Guibin Zan, John Wen, Josef Uher, Mengzhou Li, Wenbin Yun","submitted_at":"2024-06-26T04:49:34Z","abstract_excerpt":"We introduce an ultrahigh-resolution (50\\mu m\\) robotic micro-CT design for localized imaging of carotid plaques using robotic arms, cutting-edge detector, and machine learning technologies. To combat geometric error-induced artifacts in interior CT scans, we propose a data-driven geometry estimation method that maximizes the consistency between projection data and the reprojection counterparts of a reconstructed volume. Particularly, we use a normalized cross correlation metric to overcome the projection truncation effect. Our approach is validated on a robotic CT scan of a sacrificed mouse 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":"2406.18063","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"physics.med-ph","submitted_at":"2024-06-26T04:49:34Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"4433f4edc7ca32fb1d5ac0a149fe08563a363cb6cf709ceb44ccf8927a15c19b","abstract_canon_sha256":"a8d297b931fd1b5e8983a84528bc0c477b6d4aa3ce9984b9f1de53c385b68b20"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:36:54.543471Z","signature_b64":"Qj3UYjIzNkB70Jao0Sp2TgT3LEoBDOig3W5BEngIPly6TF138JCLQM0a9ubmt9O/UU454+9XHKj94wt3s8TWBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c465f822c1095e6b119110d0f03f4910e4e5605e1c3521f0b472992d8e6ab96","last_reissued_at":"2026-07-05T08:36:54.542930Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:36:54.542930Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data-driven imaging geometric recovery of ultrahigh resolution robotic micro-CT for in-vivo and other applications","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"physics.med-ph","authors_text":"Ge Wang, Guibin Zan, John Wen, Josef Uher, Mengzhou Li, Wenbin Yun","submitted_at":"2024-06-26T04:49:34Z","abstract_excerpt":"We introduce an ultrahigh-resolution (50\\mu m\\) robotic micro-CT design for localized imaging of carotid plaques using robotic arms, cutting-edge detector, and machine learning technologies. To combat geometric error-induced artifacts in interior CT scans, we propose a data-driven geometry estimation method that maximizes the consistency between projection data and the reprojection counterparts of a reconstructed volume. Particularly, we use a normalized cross correlation metric to overcome the projection truncation effect. Our approach is validated on a robotic CT scan of a sacrificed mouse a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.18063","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/2406.18063/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":"2406.18063","created_at":"2026-07-05T08:36:54.543006+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.18063v1","created_at":"2026-07-05T08:36:54.543006+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.18063","created_at":"2026-07-05T08:36:54.543006+00:00"},{"alias_kind":"pith_short_12","alias_value":"DRDF7ARMCCK6","created_at":"2026-07-05T08:36:54.543006+00:00"},{"alias_kind":"pith_short_16","alias_value":"DRDF7ARMCCK6NMIZ","created_at":"2026-07-05T08:36:54.543006+00:00"},{"alias_kind":"pith_short_8","alias_value":"DRDF7ARM","created_at":"2026-07-05T08:36:54.543006+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/DRDF7ARMCCK6NMIZCEGQ6A7USE","json":"https://pith.science/pith/DRDF7ARMCCK6NMIZCEGQ6A7USE.json","graph_json":"https://pith.science/api/pith-number/DRDF7ARMCCK6NMIZCEGQ6A7USE/graph.json","events_json":"https://pith.science/api/pith-number/DRDF7ARMCCK6NMIZCEGQ6A7USE/events.json","paper":"https://pith.science/paper/DRDF7ARM"},"agent_actions":{"view_html":"https://pith.science/pith/DRDF7ARMCCK6NMIZCEGQ6A7USE","download_json":"https://pith.science/pith/DRDF7ARMCCK6NMIZCEGQ6A7USE.json","view_paper":"https://pith.science/paper/DRDF7ARM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.18063&json=true","fetch_graph":"https://pith.science/api/pith-number/DRDF7ARMCCK6NMIZCEGQ6A7USE/graph.json","fetch_events":"https://pith.science/api/pith-number/DRDF7ARMCCK6NMIZCEGQ6A7USE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DRDF7ARMCCK6NMIZCEGQ6A7USE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DRDF7ARMCCK6NMIZCEGQ6A7USE/action/storage_attestation","attest_author":"https://pith.science/pith/DRDF7ARMCCK6NMIZCEGQ6A7USE/action/author_attestation","sign_citation":"https://pith.science/pith/DRDF7ARMCCK6NMIZCEGQ6A7USE/action/citation_signature","submit_replication":"https://pith.science/pith/DRDF7ARMCCK6NMIZCEGQ6A7USE/action/replication_record"}},"created_at":"2026-07-05T08:36:54.543006+00:00","updated_at":"2026-07-05T08:36:54.543006+00:00"}