{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:5IBRBCAGY4UUICAJ25TZZRJENW","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":"28fb8fa84ed257d31e13c41ba18e53daf634280e9902fd012e3001873b3c1930","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-05-19T14:27:44Z","title_canon_sha256":"26bc9b70fd570c1183f87003d3b3e804a8ff4968c3674615042e983e7bd35f48"},"schema_version":"1.0","source":{"id":"2205.09587","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.09587","created_at":"2026-07-05T04:24:46Z"},{"alias_kind":"arxiv_version","alias_value":"2205.09587v1","created_at":"2026-07-05T04:24:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.09587","created_at":"2026-07-05T04:24:46Z"},{"alias_kind":"pith_short_12","alias_value":"5IBRBCAGY4UU","created_at":"2026-07-05T04:24:46Z"},{"alias_kind":"pith_short_16","alias_value":"5IBRBCAGY4UUICAJ","created_at":"2026-07-05T04:24:46Z"},{"alias_kind":"pith_short_8","alias_value":"5IBRBCAG","created_at":"2026-07-05T04:24:46Z"}],"graph_snapshots":[{"event_id":"sha256:7aad644686fdbe962fbb187ff30d613c408a75d73dde1452aaa800aecd02d26c","target":"graph","created_at":"2026-07-05T04:24:46Z","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/2205.09587/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent application of deep learning methods for image reconstruction provides a successful data-driven approach to addressing the challenges when reconstructing images with measurement undersampling or various types of noise. In this work, we propose a hybrid supervised-unsupervised learning framework for X-ray computed tomography (CT) image reconstruction. The proposed learning formulation leverages both sparsity or unsupervised learning-based priors and neural network recon","authors_text":"Ling Chen, Saiprasad Ravishankar, Yong Long, Zhishen Huang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-05-19T14:27:44Z","title":"Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.09587","kind":"arxiv","version":1},"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":"sha256:49bd288574a440caa7479a9bc7162f5f63e92e9545f4c2b292f75e0204b3b9e4","target":"record","created_at":"2026-07-05T04:24:46Z","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":"28fb8fa84ed257d31e13c41ba18e53daf634280e9902fd012e3001873b3c1930","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-05-19T14:27:44Z","title_canon_sha256":"26bc9b70fd570c1183f87003d3b3e804a8ff4968c3674615042e983e7bd35f48"},"schema_version":"1.0","source":{"id":"2205.09587","kind":"arxiv","version":1}},"canonical_sha256":"ea03108806c729440809d7679cc5246d89a2763249a97a2941a092607a8f2925","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ea03108806c729440809d7679cc5246d89a2763249a97a2941a092607a8f2925","first_computed_at":"2026-07-05T04:24:46.445773Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:24:46.445773Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xkqiwQt+QEvFu1Uo8nA3fdcbd2nna3ElXYghFogd3Tdi9LQgsVuNacoCE7os/7g/N4/GS9JRtAeup+uL8V2dAg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:24:46.446165Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.09587","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:49bd288574a440caa7479a9bc7162f5f63e92e9545f4c2b292f75e0204b3b9e4","sha256:7aad644686fdbe962fbb187ff30d613c408a75d73dde1452aaa800aecd02d26c"],"state_sha256":"f08aa50c48df39278e3dbbf17de42b08fa3091b12f6e8c8440320e57226e073d"}