{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4HTCJWTGN5KBAWI3XV6WICKAOS","short_pith_number":"pith:4HTCJWTG","schema_version":"1.0","canonical_sha256":"e1e624da666f5410591bbd7d64094074a55bb2336dc469df57cadd243ca5d1d5","source":{"kind":"arxiv","id":"1903.09811","version":1},"attestation_state":"computed","paper":{"title":"Projection Super-resolution Based on Convolutional Neural Network for Computed Tomography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.med-ph","authors_text":"Ailong Cai, Bin Yan, Chao Tang, Lei Li, Linyuan Wang, Ningning Liang, Wenkun Zhang, Ziheng Li","submitted_at":"2019-03-23T12:09:22Z","abstract_excerpt":"The improvement of computed tomography (CT) image resolution is beneficial to the subsequent medical diagnosis, but it is usually limited by the scanning devices and great expense. Convolutional neural network (CNN)-based methods have achieved promising ability in super-resolution. However, existing methods mainly focus on the super-resolution of reconstructed image and do not fully explored the approach of super-resolution from projection-domain. In this paper, we studied the characteristic of projection and proposed a CNN-based super-resolution method to establish the mapping relationship of"},"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":"1903.09811","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2019-03-23T12:09:22Z","cross_cats_sorted":[],"title_canon_sha256":"fef30174dcd3e81e3aa198ff66e69f966b4d471a456a8393e27ca0d3ef440437","abstract_canon_sha256":"48d54166d225c9204d351e869fd4f638d95ba28c1a945ba0484748d90e5e0ebe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:35.444694Z","signature_b64":"5AYsNHkGCqh57uB3n4HHJR4xcH8C/sXFZ18JckPdUu9I5AhV0wAwxxAti6+7qKJXAFKI9kPKEiiAi1Tk21mYAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1e624da666f5410591bbd7d64094074a55bb2336dc469df57cadd243ca5d1d5","last_reissued_at":"2026-05-17T23:50:35.444069Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:35.444069Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Projection Super-resolution Based on Convolutional Neural Network for Computed Tomography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.med-ph","authors_text":"Ailong Cai, Bin Yan, Chao Tang, Lei Li, Linyuan Wang, Ningning Liang, Wenkun Zhang, Ziheng Li","submitted_at":"2019-03-23T12:09:22Z","abstract_excerpt":"The improvement of computed tomography (CT) image resolution is beneficial to the subsequent medical diagnosis, but it is usually limited by the scanning devices and great expense. Convolutional neural network (CNN)-based methods have achieved promising ability in super-resolution. However, existing methods mainly focus on the super-resolution of reconstructed image and do not fully explored the approach of super-resolution from projection-domain. In this paper, we studied the characteristic of projection and proposed a CNN-based super-resolution method to establish the mapping relationship of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.09811","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":""},"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":"1903.09811","created_at":"2026-05-17T23:50:35.444145+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.09811v1","created_at":"2026-05-17T23:50:35.444145+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.09811","created_at":"2026-05-17T23:50:35.444145+00:00"},{"alias_kind":"pith_short_12","alias_value":"4HTCJWTGN5KB","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"4HTCJWTGN5KBAWI3","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"4HTCJWTG","created_at":"2026-05-18T12:33:10.108867+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/4HTCJWTGN5KBAWI3XV6WICKAOS","json":"https://pith.science/pith/4HTCJWTGN5KBAWI3XV6WICKAOS.json","graph_json":"https://pith.science/api/pith-number/4HTCJWTGN5KBAWI3XV6WICKAOS/graph.json","events_json":"https://pith.science/api/pith-number/4HTCJWTGN5KBAWI3XV6WICKAOS/events.json","paper":"https://pith.science/paper/4HTCJWTG"},"agent_actions":{"view_html":"https://pith.science/pith/4HTCJWTGN5KBAWI3XV6WICKAOS","download_json":"https://pith.science/pith/4HTCJWTGN5KBAWI3XV6WICKAOS.json","view_paper":"https://pith.science/paper/4HTCJWTG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.09811&json=true","fetch_graph":"https://pith.science/api/pith-number/4HTCJWTGN5KBAWI3XV6WICKAOS/graph.json","fetch_events":"https://pith.science/api/pith-number/4HTCJWTGN5KBAWI3XV6WICKAOS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4HTCJWTGN5KBAWI3XV6WICKAOS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4HTCJWTGN5KBAWI3XV6WICKAOS/action/storage_attestation","attest_author":"https://pith.science/pith/4HTCJWTGN5KBAWI3XV6WICKAOS/action/author_attestation","sign_citation":"https://pith.science/pith/4HTCJWTGN5KBAWI3XV6WICKAOS/action/citation_signature","submit_replication":"https://pith.science/pith/4HTCJWTGN5KBAWI3XV6WICKAOS/action/replication_record"}},"created_at":"2026-05-17T23:50:35.444145+00:00","updated_at":"2026-05-17T23:50:35.444145+00:00"}