{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5HTYBDGXEPIRX2STGGK3RCW6AE","short_pith_number":"pith:5HTYBDGX","schema_version":"1.0","canonical_sha256":"e9e7808cd723d11bea533195b88ade011c61d970dea2953b8560f29c6647e6ad","source":{"kind":"arxiv","id":"2602.12755","version":3},"attestation_state":"computed","paper":{"title":"Towards reconstructing experimental sparse-view X-ray CT data with diffusion models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Diffusion priors trained on diverse synthetic data reconstruct experimental sparse-view X-ray CT scans better than narrow priors, with annealing reducing mismatch artifacts.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ezgi Demircan-Tureyen, Felix Lucka, Nelas J. Thomsen, Xinyuan Wang","submitted_at":"2026-02-13T09:33:39Z","abstract_excerpt":"Diffusion-based image generators are promising priors for ill-posed inverse problems like sparse-view X-ray Computed Tomography (CT). As most studies consider synthetic data, it is not clear whether training data mismatch (``domain shift'') or forward model mismatch complicate their successful application to experimental data. We measured CT data from a physical phantom resembling the synthetic Shepp-Logan phantom and trained diffusion priors on synthetic image data sets with different degrees of domain shift towards it. Then, we employed the priors in a Decomposed Diffusion Sampling scheme on"},"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":"2602.12755","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-02-13T09:33:39Z","cross_cats_sorted":[],"title_canon_sha256":"01806d0c3653e085497cea95266a2a95c4acfba1475460323b394ced9935264f","abstract_canon_sha256":"659cd5cbc58dc8d3d7b8437c2244f666cfb9dca6208150ea35aae843b630526b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:25.368115Z","signature_b64":"Be4dooz7493gqNxltH0/KTdUjCbIk8bCB+ae31JJICQzmsxSv+7DGjWlJgykRzyphbxdz+ZwcrXSGSkEz7dRAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9e7808cd723d11bea533195b88ade011c61d970dea2953b8560f29c6647e6ad","last_reissued_at":"2026-05-20T00:04:25.367337Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:25.367337Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards reconstructing experimental sparse-view X-ray CT data with diffusion models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Diffusion priors trained on diverse synthetic data reconstruct experimental sparse-view X-ray CT scans better than narrow priors, with annealing reducing mismatch artifacts.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ezgi Demircan-Tureyen, Felix Lucka, Nelas J. Thomsen, Xinyuan Wang","submitted_at":"2026-02-13T09:33:39Z","abstract_excerpt":"Diffusion-based image generators are promising priors for ill-posed inverse problems like sparse-view X-ray Computed Tomography (CT). As most studies consider synthetic data, it is not clear whether training data mismatch (``domain shift'') or forward model mismatch complicate their successful application to experimental data. We measured CT data from a physical phantom resembling the synthetic Shepp-Logan phantom and trained diffusion priors on synthetic image data sets with different degrees of domain shift towards it. Then, we employed the priors in a Decomposed Diffusion Sampling scheme on"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Our results reveal that domain shift plays a nuanced role: while severe mismatch causes model collapse and hallucinations, diverse priors outperform well-matched but narrow priors. Forward model mismatch pulls the image samples away from the prior manifold, which causes artifacts but can be mitigated with annealed likelihood schedules.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The physical phantom sufficiently resembles the synthetic Shepp-Logan phantom and that the Decomposed Diffusion Sampling scheme correctly balances the learned prior against the real forward model without introducing unaccounted biases in the experimental setting.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Diffusion priors trained on diverse synthetic data outperform narrow matched priors for experimental sparse-view CT reconstruction, but forward model mismatch introduces artifacts that annealed likelihood schedules can mitigate.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Diffusion priors trained on diverse synthetic data reconstruct experimental sparse-view X-ray CT scans better than narrow priors, with annealing reducing mismatch artifacts.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d3a910b2086e9dc88c113f5149945cc286e3f736587a18f9511c86949bb56d31"},"source":{"id":"2602.12755","kind":"arxiv","version":3},"verdict":{"id":"c1b51896-1b69-44e1-beab-7a153a9b7a1d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T22:39:42.136746Z","strongest_claim":"Our results reveal that domain shift plays a nuanced role: while severe mismatch causes model collapse and hallucinations, diverse priors outperform well-matched but narrow priors. Forward model mismatch pulls the image samples away from the prior manifold, which causes artifacts but can be mitigated with annealed likelihood schedules.","one_line_summary":"Diffusion priors trained on diverse synthetic data outperform narrow matched priors for experimental sparse-view CT reconstruction, but forward model mismatch introduces artifacts that annealed likelihood schedules can mitigate.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The physical phantom sufficiently resembles the synthetic Shepp-Logan phantom and that the Decomposed Diffusion Sampling scheme correctly balances the learned prior against the real forward model without introducing unaccounted biases in the experimental setting.","pith_extraction_headline":"Diffusion priors trained on diverse synthetic data reconstruct experimental sparse-view X-ray CT scans better than narrow priors, with annealing reducing mismatch artifacts."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2602.12755/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":"2602.12755","created_at":"2026-05-20T00:04:25.367467+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.12755v3","created_at":"2026-05-20T00:04:25.367467+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.12755","created_at":"2026-05-20T00:04:25.367467+00:00"},{"alias_kind":"pith_short_12","alias_value":"5HTYBDGXEPIR","created_at":"2026-05-20T00:04:25.367467+00:00"},{"alias_kind":"pith_short_16","alias_value":"5HTYBDGXEPIRX2ST","created_at":"2026-05-20T00:04:25.367467+00:00"},{"alias_kind":"pith_short_8","alias_value":"5HTYBDGX","created_at":"2026-05-20T00:04:25.367467+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/5HTYBDGXEPIRX2STGGK3RCW6AE","json":"https://pith.science/pith/5HTYBDGXEPIRX2STGGK3RCW6AE.json","graph_json":"https://pith.science/api/pith-number/5HTYBDGXEPIRX2STGGK3RCW6AE/graph.json","events_json":"https://pith.science/api/pith-number/5HTYBDGXEPIRX2STGGK3RCW6AE/events.json","paper":"https://pith.science/paper/5HTYBDGX"},"agent_actions":{"view_html":"https://pith.science/pith/5HTYBDGXEPIRX2STGGK3RCW6AE","download_json":"https://pith.science/pith/5HTYBDGXEPIRX2STGGK3RCW6AE.json","view_paper":"https://pith.science/paper/5HTYBDGX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.12755&json=true","fetch_graph":"https://pith.science/api/pith-number/5HTYBDGXEPIRX2STGGK3RCW6AE/graph.json","fetch_events":"https://pith.science/api/pith-number/5HTYBDGXEPIRX2STGGK3RCW6AE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5HTYBDGXEPIRX2STGGK3RCW6AE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5HTYBDGXEPIRX2STGGK3RCW6AE/action/storage_attestation","attest_author":"https://pith.science/pith/5HTYBDGXEPIRX2STGGK3RCW6AE/action/author_attestation","sign_citation":"https://pith.science/pith/5HTYBDGXEPIRX2STGGK3RCW6AE/action/citation_signature","submit_replication":"https://pith.science/pith/5HTYBDGXEPIRX2STGGK3RCW6AE/action/replication_record"}},"created_at":"2026-05-20T00:04:25.367467+00:00","updated_at":"2026-05-20T00:04:25.367467+00:00"}