{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:7VSZYMCEYIEGSISPQGOEWYDTJL","short_pith_number":"pith:7VSZYMCE","schema_version":"1.0","canonical_sha256":"fd659c3044c20869224f819c4b60734af2f5116c5a77d84fd0fa6df9bd97ef17","source":{"kind":"arxiv","id":"2211.10388","version":1},"attestation_state":"computed","paper":{"title":"Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP","physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Ge Wang, Wenjun Xia, Wenxiang Cong","submitted_at":"2022-11-18T17:35:36Z","abstract_excerpt":"Sparse-view computed tomography (CT) can be used to reduce radiation dose greatly but is suffers from severe image artifacts. Recently, the deep learning based method for sparse-view CT reconstruction has attracted a major attention. However, neural networks often have a limited ability to remove the artifacts when they only work in the image domain. Deep learning-based sinogram processing can achieve a better anti-artifact performance, but it inevitably requires feature maps of the whole image in a video memory, which makes handling large-scale or three-dimensional (3D) images rather challeng"},"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":"2211.10388","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-11-18T17:35:36Z","cross_cats_sorted":["cs.LG","eess.SP","physics.med-ph"],"title_canon_sha256":"a8fd5bc7e038a330decbf638b0f1a0c2a2a343a28385b9fe592000f097bd10fe","abstract_canon_sha256":"9a71882521303aca5decb74ab7867f533ff1aeac4baa575012881e807b8dfd63"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:17:21.631943Z","signature_b64":"sD1DWykKdMSnFk6Rs46bZJj0XG+YPfmdo9b7ns3BrUgIRLU6Bg07howjc15j7EhlbW5wU9VFcbpq9ZYMBIW+Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd659c3044c20869224f819c4b60734af2f5116c5a77d84fd0fa6df9bd97ef17","last_reissued_at":"2026-07-05T05:17:21.631597Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:17:21.631597Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP","physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Ge Wang, Wenjun Xia, Wenxiang Cong","submitted_at":"2022-11-18T17:35:36Z","abstract_excerpt":"Sparse-view computed tomography (CT) can be used to reduce radiation dose greatly but is suffers from severe image artifacts. Recently, the deep learning based method for sparse-view CT reconstruction has attracted a major attention. However, neural networks often have a limited ability to remove the artifacts when they only work in the image domain. Deep learning-based sinogram processing can achieve a better anti-artifact performance, but it inevitably requires feature maps of the whole image in a video memory, which makes handling large-scale or three-dimensional (3D) images rather challeng"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.10388","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/2211.10388/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":"2211.10388","created_at":"2026-07-05T05:17:21.631654+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.10388v1","created_at":"2026-07-05T05:17:21.631654+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.10388","created_at":"2026-07-05T05:17:21.631654+00:00"},{"alias_kind":"pith_short_12","alias_value":"7VSZYMCEYIEG","created_at":"2026-07-05T05:17:21.631654+00:00"},{"alias_kind":"pith_short_16","alias_value":"7VSZYMCEYIEGSISP","created_at":"2026-07-05T05:17:21.631654+00:00"},{"alias_kind":"pith_short_8","alias_value":"7VSZYMCE","created_at":"2026-07-05T05:17:21.631654+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.02149","citing_title":"Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine","ref_index":77,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7VSZYMCEYIEGSISPQGOEWYDTJL","json":"https://pith.science/pith/7VSZYMCEYIEGSISPQGOEWYDTJL.json","graph_json":"https://pith.science/api/pith-number/7VSZYMCEYIEGSISPQGOEWYDTJL/graph.json","events_json":"https://pith.science/api/pith-number/7VSZYMCEYIEGSISPQGOEWYDTJL/events.json","paper":"https://pith.science/paper/7VSZYMCE"},"agent_actions":{"view_html":"https://pith.science/pith/7VSZYMCEYIEGSISPQGOEWYDTJL","download_json":"https://pith.science/pith/7VSZYMCEYIEGSISPQGOEWYDTJL.json","view_paper":"https://pith.science/paper/7VSZYMCE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.10388&json=true","fetch_graph":"https://pith.science/api/pith-number/7VSZYMCEYIEGSISPQGOEWYDTJL/graph.json","fetch_events":"https://pith.science/api/pith-number/7VSZYMCEYIEGSISPQGOEWYDTJL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7VSZYMCEYIEGSISPQGOEWYDTJL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7VSZYMCEYIEGSISPQGOEWYDTJL/action/storage_attestation","attest_author":"https://pith.science/pith/7VSZYMCEYIEGSISPQGOEWYDTJL/action/author_attestation","sign_citation":"https://pith.science/pith/7VSZYMCEYIEGSISPQGOEWYDTJL/action/citation_signature","submit_replication":"https://pith.science/pith/7VSZYMCEYIEGSISPQGOEWYDTJL/action/replication_record"}},"created_at":"2026-07-05T05:17:21.631654+00:00","updated_at":"2026-07-05T05:17:21.631654+00:00"}