{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:I5WP7HTKFKY2ATM2BMTHX2I22F","short_pith_number":"pith:I5WP7HTK","schema_version":"1.0","canonical_sha256":"476cff9e6a2ab1a04d9a0b267be91ad14d0e94791f4e42eca99a0b09097c5022","source":{"kind":"arxiv","id":"2309.14367","version":1},"attestation_state":"computed","paper":{"title":"Design of Novel Loss Functions for Deep Learning in X-ray CT","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Brian Nett, Charles A. Bouman, Jie Tang, Ken D. Sauer, Madhuri Nagare, Obaidullah Rahman, Roman Melnyk","submitted_at":"2023-09-23T15:39:28Z","abstract_excerpt":"Deep learning (DL) shows promise of advantages over conventional signal processing techniques in a variety of imaging applications. The networks' being trained from examples of data rather than explicitly designed allows them to learn signal and noise characteristics to most effectively construct a mapping from corrupted data to higher quality representations. In inverse problems, one has options of applying DL in the domain of the originally captured data, in the transformed domain of the desired final representation, or both.\n  X-ray computed tomography (CT), one of the most valuable tools i"},"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":"2309.14367","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2023-09-23T15:39:28Z","cross_cats_sorted":[],"title_canon_sha256":"6e1cfa592eb4f535d15214ca51e91a7e5809ed1fc0743748110e2750bc2142f4","abstract_canon_sha256":"4656ca644cd8865be73f1e39d510ddbd6670f77c1dd149567accfa76f7f0dcba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:54:24.524096Z","signature_b64":"inGrdp26o6ueSGgZZnx6mFSucCFYxAa7EnW5jLdgjhg7VeoupJ8/pJoPJoilzKdg99gZYcb3foUVj3Mjpk+4Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"476cff9e6a2ab1a04d9a0b267be91ad14d0e94791f4e42eca99a0b09097c5022","last_reissued_at":"2026-07-05T06:54:24.523576Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:54:24.523576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Design of Novel Loss Functions for Deep Learning in X-ray CT","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Brian Nett, Charles A. Bouman, Jie Tang, Ken D. Sauer, Madhuri Nagare, Obaidullah Rahman, Roman Melnyk","submitted_at":"2023-09-23T15:39:28Z","abstract_excerpt":"Deep learning (DL) shows promise of advantages over conventional signal processing techniques in a variety of imaging applications. The networks' being trained from examples of data rather than explicitly designed allows them to learn signal and noise characteristics to most effectively construct a mapping from corrupted data to higher quality representations. In inverse problems, one has options of applying DL in the domain of the originally captured data, in the transformed domain of the desired final representation, or both.\n  X-ray computed tomography (CT), one of the most valuable tools i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.14367","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/2309.14367/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":"2309.14367","created_at":"2026-07-05T06:54:24.523645+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.14367v1","created_at":"2026-07-05T06:54:24.523645+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.14367","created_at":"2026-07-05T06:54:24.523645+00:00"},{"alias_kind":"pith_short_12","alias_value":"I5WP7HTKFKY2","created_at":"2026-07-05T06:54:24.523645+00:00"},{"alias_kind":"pith_short_16","alias_value":"I5WP7HTKFKY2ATM2","created_at":"2026-07-05T06:54:24.523645+00:00"},{"alias_kind":"pith_short_8","alias_value":"I5WP7HTK","created_at":"2026-07-05T06:54:24.523645+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/I5WP7HTKFKY2ATM2BMTHX2I22F","json":"https://pith.science/pith/I5WP7HTKFKY2ATM2BMTHX2I22F.json","graph_json":"https://pith.science/api/pith-number/I5WP7HTKFKY2ATM2BMTHX2I22F/graph.json","events_json":"https://pith.science/api/pith-number/I5WP7HTKFKY2ATM2BMTHX2I22F/events.json","paper":"https://pith.science/paper/I5WP7HTK"},"agent_actions":{"view_html":"https://pith.science/pith/I5WP7HTKFKY2ATM2BMTHX2I22F","download_json":"https://pith.science/pith/I5WP7HTKFKY2ATM2BMTHX2I22F.json","view_paper":"https://pith.science/paper/I5WP7HTK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.14367&json=true","fetch_graph":"https://pith.science/api/pith-number/I5WP7HTKFKY2ATM2BMTHX2I22F/graph.json","fetch_events":"https://pith.science/api/pith-number/I5WP7HTKFKY2ATM2BMTHX2I22F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I5WP7HTKFKY2ATM2BMTHX2I22F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I5WP7HTKFKY2ATM2BMTHX2I22F/action/storage_attestation","attest_author":"https://pith.science/pith/I5WP7HTKFKY2ATM2BMTHX2I22F/action/author_attestation","sign_citation":"https://pith.science/pith/I5WP7HTKFKY2ATM2BMTHX2I22F/action/citation_signature","submit_replication":"https://pith.science/pith/I5WP7HTKFKY2ATM2BMTHX2I22F/action/replication_record"}},"created_at":"2026-07-05T06:54:24.523645+00:00","updated_at":"2026-07-05T06:54:24.523645+00:00"}