{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:CTQIAKPCUMRZF46A5KAJA7SSOB","short_pith_number":"pith:CTQIAKPC","schema_version":"1.0","canonical_sha256":"14e08029e2a32392f3c0ea80907e52707f60501bb4054711c8dd44fd6f6beb84","source":{"kind":"arxiv","id":"2103.00634","version":4},"attestation_state":"computed","paper":{"title":"TransCT: Dual-path Transformer for Low Dose Computed Tomography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Lei Xing, Lequan Yu, Wei Zhao, Xiaokun Liang, Zhicheng Zhang","submitted_at":"2021-02-28T21:46:54Z","abstract_excerpt":"Low dose computed tomography (LDCT) has attracted more and more attention in routine clinical diagnosis assessment, therapy planning, etc., which can reduce the dose of X-ray radiation to patients. However, the noise caused by low X-ray exposure degrades the CT image quality and then affects clinical diagnosis accuracy. In this paper, we train a transformer-based neural network to enhance the final CT image quality. To be specific, we first decompose the noisy LDCT image into two parts: high-frequency (HF) and low-frequency (LF) compositions. Then, we extract content features (X_{L_c}) and lat"},"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":"2103.00634","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-02-28T21:46:54Z","cross_cats_sorted":["physics.med-ph"],"title_canon_sha256":"048b1dc45e799628d5e8cf9fea25eb4884ad3ae638b99fad5bb1f7c68c72349d","abstract_canon_sha256":"8fc9b8a5cf82b24206a620a6237560ddb1e05722f3045ebb5c9d7a108e5a08cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:54:54.847011Z","signature_b64":"TCES4+Tl5Go1KB8E5FmAhx2SI0An/Qjo3KI6yQGI8apReyqMCGHJXYMy9PpB7/66sKgYH8i0fVvRoIYcGabhAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14e08029e2a32392f3c0ea80907e52707f60501bb4054711c8dd44fd6f6beb84","last_reissued_at":"2026-07-05T02:54:54.846533Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:54:54.846533Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TransCT: Dual-path Transformer for Low Dose Computed Tomography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Lei Xing, Lequan Yu, Wei Zhao, Xiaokun Liang, Zhicheng Zhang","submitted_at":"2021-02-28T21:46:54Z","abstract_excerpt":"Low dose computed tomography (LDCT) has attracted more and more attention in routine clinical diagnosis assessment, therapy planning, etc., which can reduce the dose of X-ray radiation to patients. However, the noise caused by low X-ray exposure degrades the CT image quality and then affects clinical diagnosis accuracy. In this paper, we train a transformer-based neural network to enhance the final CT image quality. To be specific, we first decompose the noisy LDCT image into two parts: high-frequency (HF) and low-frequency (LF) compositions. Then, we extract content features (X_{L_c}) and lat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.00634","kind":"arxiv","version":4},"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/2103.00634/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":"2103.00634","created_at":"2026-07-05T02:54:54.846591+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.00634v4","created_at":"2026-07-05T02:54:54.846591+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.00634","created_at":"2026-07-05T02:54:54.846591+00:00"},{"alias_kind":"pith_short_12","alias_value":"CTQIAKPCUMRZ","created_at":"2026-07-05T02:54:54.846591+00:00"},{"alias_kind":"pith_short_16","alias_value":"CTQIAKPCUMRZF46A","created_at":"2026-07-05T02:54:54.846591+00:00"},{"alias_kind":"pith_short_8","alias_value":"CTQIAKPC","created_at":"2026-07-05T02:54:54.846591+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/CTQIAKPCUMRZF46A5KAJA7SSOB","json":"https://pith.science/pith/CTQIAKPCUMRZF46A5KAJA7SSOB.json","graph_json":"https://pith.science/api/pith-number/CTQIAKPCUMRZF46A5KAJA7SSOB/graph.json","events_json":"https://pith.science/api/pith-number/CTQIAKPCUMRZF46A5KAJA7SSOB/events.json","paper":"https://pith.science/paper/CTQIAKPC"},"agent_actions":{"view_html":"https://pith.science/pith/CTQIAKPCUMRZF46A5KAJA7SSOB","download_json":"https://pith.science/pith/CTQIAKPCUMRZF46A5KAJA7SSOB.json","view_paper":"https://pith.science/paper/CTQIAKPC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.00634&json=true","fetch_graph":"https://pith.science/api/pith-number/CTQIAKPCUMRZF46A5KAJA7SSOB/graph.json","fetch_events":"https://pith.science/api/pith-number/CTQIAKPCUMRZF46A5KAJA7SSOB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CTQIAKPCUMRZF46A5KAJA7SSOB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CTQIAKPCUMRZF46A5KAJA7SSOB/action/storage_attestation","attest_author":"https://pith.science/pith/CTQIAKPCUMRZF46A5KAJA7SSOB/action/author_attestation","sign_citation":"https://pith.science/pith/CTQIAKPCUMRZF46A5KAJA7SSOB/action/citation_signature","submit_replication":"https://pith.science/pith/CTQIAKPCUMRZF46A5KAJA7SSOB/action/replication_record"}},"created_at":"2026-07-05T02:54:54.846591+00:00","updated_at":"2026-07-05T02:54:54.846591+00:00"}