{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FGVZA5ME5LHJOJOQDTLDGVDCQ3","short_pith_number":"pith:FGVZA5ME","schema_version":"1.0","canonical_sha256":"29ab907584eace9725d01cd633546286caedbaba7f9b90f0ad7a50d8d8f4aa9a","source":{"kind":"arxiv","id":"2606.05381","version":1},"attestation_state":"computed","paper":{"title":"Generalized TV--$\\ell_p$ Structured Priors for Bayesian $T_1$ Mapping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Disi Lin, Martin Berggren, Tommy L\\\"ofstedt","submitted_at":"2026-06-03T19:30:42Z","abstract_excerpt":"We propose an extended family of structured spatial priors that incorporates the total variation (TV) function with $\\ell_p$ norms. The prior is proven to be proper and incorporated into a Bayesian regression framework to enable uncertainty quantification in $T_1$ mapping, with posterior inference performed using the No-U-Turn Sampler (NUTS). This TV--$\\ell_p$ construction is proven to constitute a well-defined family of prior distributions, and it naturally enforces spatial consistency and smooth variations in the estimated parameter maps. The method was evaluated in comparison to maximum-lik"},"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":"2606.05381","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-03T19:30:42Z","cross_cats_sorted":[],"title_canon_sha256":"10497e45d68aa3884284ba88ffc1485902d71e41760a13904b0169197afa6478","abstract_canon_sha256":"cf9c5de570b09666617882c64dd3f043829fe51b55d4ebced5c2ae07107a73a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T00:13:55.947787Z","signature_b64":"IdTnytmhWUM0QDxRxDc6DiNnw/6LIaSU+uqXdfvpxMFvKHbhY23SHVNd6isYhumHxSU+jHbfiFomDJscMZWtBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"29ab907584eace9725d01cd633546286caedbaba7f9b90f0ad7a50d8d8f4aa9a","last_reissued_at":"2026-06-05T00:13:55.947271Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T00:13:55.947271Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Generalized TV--$\\ell_p$ Structured Priors for Bayesian $T_1$ Mapping","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Disi Lin, Martin Berggren, Tommy L\\\"ofstedt","submitted_at":"2026-06-03T19:30:42Z","abstract_excerpt":"We propose an extended family of structured spatial priors that incorporates the total variation (TV) function with $\\ell_p$ norms. The prior is proven to be proper and incorporated into a Bayesian regression framework to enable uncertainty quantification in $T_1$ mapping, with posterior inference performed using the No-U-Turn Sampler (NUTS). This TV--$\\ell_p$ construction is proven to constitute a well-defined family of prior distributions, and it naturally enforces spatial consistency and smooth variations in the estimated parameter maps. The method was evaluated in comparison to maximum-lik"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05381","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/2606.05381/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":"2606.05381","created_at":"2026-06-05T00:13:55.947362+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.05381v1","created_at":"2026-06-05T00:13:55.947362+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05381","created_at":"2026-06-05T00:13:55.947362+00:00"},{"alias_kind":"pith_short_12","alias_value":"FGVZA5ME5LHJ","created_at":"2026-06-05T00:13:55.947362+00:00"},{"alias_kind":"pith_short_16","alias_value":"FGVZA5ME5LHJOJOQ","created_at":"2026-06-05T00:13:55.947362+00:00"},{"alias_kind":"pith_short_8","alias_value":"FGVZA5ME","created_at":"2026-06-05T00:13:55.947362+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/FGVZA5ME5LHJOJOQDTLDGVDCQ3","json":"https://pith.science/pith/FGVZA5ME5LHJOJOQDTLDGVDCQ3.json","graph_json":"https://pith.science/api/pith-number/FGVZA5ME5LHJOJOQDTLDGVDCQ3/graph.json","events_json":"https://pith.science/api/pith-number/FGVZA5ME5LHJOJOQDTLDGVDCQ3/events.json","paper":"https://pith.science/paper/FGVZA5ME"},"agent_actions":{"view_html":"https://pith.science/pith/FGVZA5ME5LHJOJOQDTLDGVDCQ3","download_json":"https://pith.science/pith/FGVZA5ME5LHJOJOQDTLDGVDCQ3.json","view_paper":"https://pith.science/paper/FGVZA5ME","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.05381&json=true","fetch_graph":"https://pith.science/api/pith-number/FGVZA5ME5LHJOJOQDTLDGVDCQ3/graph.json","fetch_events":"https://pith.science/api/pith-number/FGVZA5ME5LHJOJOQDTLDGVDCQ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FGVZA5ME5LHJOJOQDTLDGVDCQ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FGVZA5ME5LHJOJOQDTLDGVDCQ3/action/storage_attestation","attest_author":"https://pith.science/pith/FGVZA5ME5LHJOJOQDTLDGVDCQ3/action/author_attestation","sign_citation":"https://pith.science/pith/FGVZA5ME5LHJOJOQDTLDGVDCQ3/action/citation_signature","submit_replication":"https://pith.science/pith/FGVZA5ME5LHJOJOQDTLDGVDCQ3/action/replication_record"}},"created_at":"2026-06-05T00:13:55.947362+00:00","updated_at":"2026-06-05T00:13:55.947362+00:00"}