{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IVUN6N2J54EAI722YERKYU3ZON","short_pith_number":"pith:IVUN6N2J","schema_version":"1.0","canonical_sha256":"4568df3749ef08047f5ac122ac537973521c8128f1d90ff5b92a71af6988a5d7","source":{"kind":"arxiv","id":"2311.00412","version":1},"attestation_state":"computed","paper":{"title":"Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.med-ph"],"primary_cat":"cs.CV","authors_text":"Ge Ren, Hongfei Sun, Jiarui Zhu, Jing Cai, Jing Qin, Shaohua Zhi, Werxing Chen","submitted_at":"2023-11-01T10:09:01Z","abstract_excerpt":"Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy (IGRT) to provide updated patient anatomy information for cancer treatments. However, CBCT images often suffer from streaking artifacts and noise caused by under-rate sampling projections and low-dose exposure, resulting in low clarity and information loss. While recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts, they have limited performance on preserving anatomical details since conventional pixel-to-pixel loss functions are incapable of de"},"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":"2311.00412","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-11-01T10:09:01Z","cross_cats_sorted":["physics.med-ph"],"title_canon_sha256":"75ac7c20fb1484512d34dc763b86bd478e4ec050c46da2640fe03953a0e99d54","abstract_canon_sha256":"9c1c24d8b540d8f855e5e1becd3242c23ad8b874c595a3facbd0fd9150b43554"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:07:57.280939Z","signature_b64":"kiAaiqphZWr/2JjfLXKGB+g46WgW6anSLVoZ6fcGx/4H3b9CfjsB1Y38HML0pdXUZMTahSJurAe20USjA5FBBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4568df3749ef08047f5ac122ac537973521c8128f1d90ff5b92a71af6988a5d7","last_reissued_at":"2026-07-05T07:07:57.280370Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:07:57.280370Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Feature-oriented Deep Learning Framework for Pulmonary Cone-beam CT (CBCT) Enhancement with Multi-task Customized Perceptual Loss","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.med-ph"],"primary_cat":"cs.CV","authors_text":"Ge Ren, Hongfei Sun, Jiarui Zhu, Jing Cai, Jing Qin, Shaohua Zhi, Werxing Chen","submitted_at":"2023-11-01T10:09:01Z","abstract_excerpt":"Cone-beam computed tomography (CBCT) is routinely collected during image-guided radiation therapy (IGRT) to provide updated patient anatomy information for cancer treatments. However, CBCT images often suffer from streaking artifacts and noise caused by under-rate sampling projections and low-dose exposure, resulting in low clarity and information loss. While recent deep learning-based CBCT enhancement methods have shown promising results in suppressing artifacts, they have limited performance on preserving anatomical details since conventional pixel-to-pixel loss functions are incapable of de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.00412","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/2311.00412/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":"2311.00412","created_at":"2026-07-05T07:07:57.280436+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.00412v1","created_at":"2026-07-05T07:07:57.280436+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.00412","created_at":"2026-07-05T07:07:57.280436+00:00"},{"alias_kind":"pith_short_12","alias_value":"IVUN6N2J54EA","created_at":"2026-07-05T07:07:57.280436+00:00"},{"alias_kind":"pith_short_16","alias_value":"IVUN6N2J54EAI722","created_at":"2026-07-05T07:07:57.280436+00:00"},{"alias_kind":"pith_short_8","alias_value":"IVUN6N2J","created_at":"2026-07-05T07:07:57.280436+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/IVUN6N2J54EAI722YERKYU3ZON","json":"https://pith.science/pith/IVUN6N2J54EAI722YERKYU3ZON.json","graph_json":"https://pith.science/api/pith-number/IVUN6N2J54EAI722YERKYU3ZON/graph.json","events_json":"https://pith.science/api/pith-number/IVUN6N2J54EAI722YERKYU3ZON/events.json","paper":"https://pith.science/paper/IVUN6N2J"},"agent_actions":{"view_html":"https://pith.science/pith/IVUN6N2J54EAI722YERKYU3ZON","download_json":"https://pith.science/pith/IVUN6N2J54EAI722YERKYU3ZON.json","view_paper":"https://pith.science/paper/IVUN6N2J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.00412&json=true","fetch_graph":"https://pith.science/api/pith-number/IVUN6N2J54EAI722YERKYU3ZON/graph.json","fetch_events":"https://pith.science/api/pith-number/IVUN6N2J54EAI722YERKYU3ZON/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IVUN6N2J54EAI722YERKYU3ZON/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IVUN6N2J54EAI722YERKYU3ZON/action/storage_attestation","attest_author":"https://pith.science/pith/IVUN6N2J54EAI722YERKYU3ZON/action/author_attestation","sign_citation":"https://pith.science/pith/IVUN6N2J54EAI722YERKYU3ZON/action/citation_signature","submit_replication":"https://pith.science/pith/IVUN6N2J54EAI722YERKYU3ZON/action/replication_record"}},"created_at":"2026-07-05T07:07:57.280436+00:00","updated_at":"2026-07-05T07:07:57.280436+00:00"}