{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:WCLRQQOINB3BW72LJ7M2HSBTQ3","short_pith_number":"pith:WCLRQQOI","schema_version":"1.0","canonical_sha256":"b0971841c868761b7f4b4fd9a3c83386d64a10d180c3c691f6145fd49d28a8fd","source":{"kind":"arxiv","id":"1807.11455","version":2},"attestation_state":"computed","paper":{"title":"Factor analysis of dynamic PET images: beyond Gaussian noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","physics.data-an","stat.ML"],"primary_cat":"eess.IV","authors_text":"C\\'edric F\\'evotte, Clovis Tauber, Maria-Joao Ribeiro, Nicolas Dobigeon, Simon Stute, Thomas Oberlin, Yanna Cruz Cavalcanti","submitted_at":"2018-07-30T17:23:50Z","abstract_excerpt":"Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data. Reliable and interpretable results are possible only if considered with respect to suitable noise statistics. However, the noise in reconstructed dynamic PET images is very difficult to characterize, despite the Poissonian nature of the count-rates. Rather than explicitly modeling the noise distribution, this work proposes to study the relevance of several divergence measures to be used within a factor analysis frame"},"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":"1807.11455","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-07-30T17:23:50Z","cross_cats_sorted":["cs.CV","physics.data-an","stat.ML"],"title_canon_sha256":"cb79148014d5a9a0e9324c5c60e108b1b0c0dfcf14031b3f0830d9c816bd69c5","abstract_canon_sha256":"2d2e7d5629a8ae5884e758052d21f9cdd1f60d5ceb5dce9aeba5216ad21c4650"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:27.407103Z","signature_b64":"szf9I6lqIK0YpWEYLtNV6Ry3Z7hpA16QGLgkfoTQoxdsdCsfqBJkdA2j8Vl+qAJ1Neoy+oGJKoI+MaQvgUJlDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0971841c868761b7f4b4fd9a3c83386d64a10d180c3c691f6145fd49d28a8fd","last_reissued_at":"2026-05-17T23:50:27.406441Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:27.406441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Factor analysis of dynamic PET images: beyond Gaussian noise","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","physics.data-an","stat.ML"],"primary_cat":"eess.IV","authors_text":"C\\'edric F\\'evotte, Clovis Tauber, Maria-Joao Ribeiro, Nicolas Dobigeon, Simon Stute, Thomas Oberlin, Yanna Cruz Cavalcanti","submitted_at":"2018-07-30T17:23:50Z","abstract_excerpt":"Factor analysis has proven to be a relevant tool for extracting tissue time-activity curves (TACs) in dynamic PET images, since it allows for an unsupervised analysis of the data. Reliable and interpretable results are possible only if considered with respect to suitable noise statistics. However, the noise in reconstructed dynamic PET images is very difficult to characterize, despite the Poissonian nature of the count-rates. Rather than explicitly modeling the noise distribution, this work proposes to study the relevance of several divergence measures to be used within a factor analysis frame"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.11455","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1807.11455","created_at":"2026-05-17T23:50:27.406548+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.11455v2","created_at":"2026-05-17T23:50:27.406548+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.11455","created_at":"2026-05-17T23:50:27.406548+00:00"},{"alias_kind":"pith_short_12","alias_value":"WCLRQQOINB3B","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"WCLRQQOINB3BW72L","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"WCLRQQOI","created_at":"2026-05-18T12:32:59.047623+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/WCLRQQOINB3BW72LJ7M2HSBTQ3","json":"https://pith.science/pith/WCLRQQOINB3BW72LJ7M2HSBTQ3.json","graph_json":"https://pith.science/api/pith-number/WCLRQQOINB3BW72LJ7M2HSBTQ3/graph.json","events_json":"https://pith.science/api/pith-number/WCLRQQOINB3BW72LJ7M2HSBTQ3/events.json","paper":"https://pith.science/paper/WCLRQQOI"},"agent_actions":{"view_html":"https://pith.science/pith/WCLRQQOINB3BW72LJ7M2HSBTQ3","download_json":"https://pith.science/pith/WCLRQQOINB3BW72LJ7M2HSBTQ3.json","view_paper":"https://pith.science/paper/WCLRQQOI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.11455&json=true","fetch_graph":"https://pith.science/api/pith-number/WCLRQQOINB3BW72LJ7M2HSBTQ3/graph.json","fetch_events":"https://pith.science/api/pith-number/WCLRQQOINB3BW72LJ7M2HSBTQ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WCLRQQOINB3BW72LJ7M2HSBTQ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WCLRQQOINB3BW72LJ7M2HSBTQ3/action/storage_attestation","attest_author":"https://pith.science/pith/WCLRQQOINB3BW72LJ7M2HSBTQ3/action/author_attestation","sign_citation":"https://pith.science/pith/WCLRQQOINB3BW72LJ7M2HSBTQ3/action/citation_signature","submit_replication":"https://pith.science/pith/WCLRQQOINB3BW72LJ7M2HSBTQ3/action/replication_record"}},"created_at":"2026-05-17T23:50:27.406548+00:00","updated_at":"2026-05-17T23:50:27.406548+00:00"}