{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:J7WAVP7W6AGRAAFBLCGZ3M4NHV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2086b2e0535cfe8882d180f0794465f5c73585795f5807d18dd428e3f2eec14c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-11-12T17:03:31Z","title_canon_sha256":"0d994756fb9293776288bc051715f098b067966a572035c03e42bf15766a6fc5"},"schema_version":"1.0","source":{"id":"2411.07930","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.07930","created_at":"2026-07-05T11:31:41Z"},{"alias_kind":"arxiv_version","alias_value":"2411.07930v5","created_at":"2026-07-05T11:31:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.07930","created_at":"2026-07-05T11:31:41Z"},{"alias_kind":"pith_short_12","alias_value":"J7WAVP7W6AGR","created_at":"2026-07-05T11:31:41Z"},{"alias_kind":"pith_short_16","alias_value":"J7WAVP7W6AGRAAFB","created_at":"2026-07-05T11:31:41Z"},{"alias_kind":"pith_short_8","alias_value":"J7WAVP7W","created_at":"2026-07-05T11:31:41Z"}],"graph_snapshots":[{"event_id":"sha256:52410cc338e5bc6e798e4054d71cd801f519899f8ccef10bb04861981e67eff5","target":"graph","created_at":"2026-07-05T11:31:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.07930/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Low-dose CT (LDCT) significantly reduces the radiation dose received by patients, however, dose reduction introduces additional noise and artifacts. Currently, denoising methods based on convolutional neural networks (CNNs) face limitations in long-range modeling capabilities, while Transformer-based denoising methods, although capable of powerful long-range modeling, suffer from high computational complexity. Furthermore, the denoised images predicted by deep learning-based techniques inevitably exhibit differences in noise distribution compared to normal-dose CT (NDCT) images, which can also","authors_text":"Hongshi Huang, Jiashu Dong, Linxuan Li, Luyao Yang, Wei Zhao, Wenjia Wei, Wenwen Zhang, Yahua Liu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-11-12T17:03:31Z","title":"CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT Denoising"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.07930","kind":"arxiv","version":5},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:93f2289b542cc238e74022fa22adb33742ac22daf958646d597381174d4a3a76","target":"record","created_at":"2026-07-05T11:31:41Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2086b2e0535cfe8882d180f0794465f5c73585795f5807d18dd428e3f2eec14c","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-11-12T17:03:31Z","title_canon_sha256":"0d994756fb9293776288bc051715f098b067966a572035c03e42bf15766a6fc5"},"schema_version":"1.0","source":{"id":"2411.07930","kind":"arxiv","version":5}},"canonical_sha256":"4fec0abff6f00d1000a1588d9db38d3d54c2f77b9ae296ab61d8e30a05daf308","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4fec0abff6f00d1000a1588d9db38d3d54c2f77b9ae296ab61d8e30a05daf308","first_computed_at":"2026-07-05T11:31:41.148459Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:31:41.148459Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wQqxRzJv8oTojQm7pLt6f38hJ/reGe08THEY1tFbEKxE0PMuvlhK46gtfLtiBPTj1Y5xoCgL2m2l2i5odP28BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:31:41.149071Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.07930","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:93f2289b542cc238e74022fa22adb33742ac22daf958646d597381174d4a3a76","sha256:52410cc338e5bc6e798e4054d71cd801f519899f8ccef10bb04861981e67eff5"],"state_sha256":"b27dd24b4f3ff8f1b60368ddfe06eb8fa7bad154e44c2cd459b31308373dabbf"}