{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:3ONNUNHSQWFZ46RUIU46MOKG2S","short_pith_number":"pith:3ONNUNHS","schema_version":"1.0","canonical_sha256":"db9ada34f2858b9e7a344539e63946d482f3115aaad58ee8f1d82e23d0fa1817","source":{"kind":"arxiv","id":"1801.07308","version":3},"attestation_state":"computed","paper":{"title":"Stochastic Proximal Gradient Algorithms for Multi-Source Quantitative Photoacoustic Tomography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.med-ph"],"primary_cat":"math.NA","authors_text":"Lukas Neumann, Markus Haltmeier, Simon Rabanser","submitted_at":"2018-01-22T20:34:58Z","abstract_excerpt":"The development of accurate and efficient image reconstruction algorithms is a central aspect of quantitative photoacoustic tomography (QPAT). In this paper, we address this issues for multi-source QPAT using the radiative transfer equation (RTE) as accurate model for light transport. The tissue parameters are jointly reconstructed from the acoustical data measured for each of the applied sources. We develop stochastic proximal gradient methods for multi-source QPAT, which are more efficient than standard proximal gradient methods in which a single iterative update has complexity proportional "},"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":"1801.07308","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2018-01-22T20:34:58Z","cross_cats_sorted":["physics.med-ph"],"title_canon_sha256":"5ab739f2d2eb408d628dab37b765336d53dc900ceef04a67f9d276af6b3db72d","abstract_canon_sha256":"6ffbb8a9e502288b309c41fd4c1a688099070ebfae92d05ce6e7cc065edb98cf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:56.489731Z","signature_b64":"ouaGDUA0+Vr6vdwvAvnSrOjI0TIdFVftuuYLQk8IWoO8MIpriqmlhFEYjeAts6eltAzoqHLusbvaVrWYd6GyCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db9ada34f2858b9e7a344539e63946d482f3115aaad58ee8f1d82e23d0fa1817","last_reissued_at":"2026-05-18T00:19:56.489138Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:56.489138Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Stochastic Proximal Gradient Algorithms for Multi-Source Quantitative Photoacoustic Tomography","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.med-ph"],"primary_cat":"math.NA","authors_text":"Lukas Neumann, Markus Haltmeier, Simon Rabanser","submitted_at":"2018-01-22T20:34:58Z","abstract_excerpt":"The development of accurate and efficient image reconstruction algorithms is a central aspect of quantitative photoacoustic tomography (QPAT). In this paper, we address this issues for multi-source QPAT using the radiative transfer equation (RTE) as accurate model for light transport. The tissue parameters are jointly reconstructed from the acoustical data measured for each of the applied sources. We develop stochastic proximal gradient methods for multi-source QPAT, which are more efficient than standard proximal gradient methods in which a single iterative update has complexity proportional "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.07308","kind":"arxiv","version":3},"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":"1801.07308","created_at":"2026-05-18T00:19:56.489239+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.07308v3","created_at":"2026-05-18T00:19:56.489239+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.07308","created_at":"2026-05-18T00:19:56.489239+00:00"},{"alias_kind":"pith_short_12","alias_value":"3ONNUNHSQWFZ","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_16","alias_value":"3ONNUNHSQWFZ46RU","created_at":"2026-05-18T12:32:02.567920+00:00"},{"alias_kind":"pith_short_8","alias_value":"3ONNUNHS","created_at":"2026-05-18T12:32:02.567920+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/3ONNUNHSQWFZ46RUIU46MOKG2S","json":"https://pith.science/pith/3ONNUNHSQWFZ46RUIU46MOKG2S.json","graph_json":"https://pith.science/api/pith-number/3ONNUNHSQWFZ46RUIU46MOKG2S/graph.json","events_json":"https://pith.science/api/pith-number/3ONNUNHSQWFZ46RUIU46MOKG2S/events.json","paper":"https://pith.science/paper/3ONNUNHS"},"agent_actions":{"view_html":"https://pith.science/pith/3ONNUNHSQWFZ46RUIU46MOKG2S","download_json":"https://pith.science/pith/3ONNUNHSQWFZ46RUIU46MOKG2S.json","view_paper":"https://pith.science/paper/3ONNUNHS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.07308&json=true","fetch_graph":"https://pith.science/api/pith-number/3ONNUNHSQWFZ46RUIU46MOKG2S/graph.json","fetch_events":"https://pith.science/api/pith-number/3ONNUNHSQWFZ46RUIU46MOKG2S/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3ONNUNHSQWFZ46RUIU46MOKG2S/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3ONNUNHSQWFZ46RUIU46MOKG2S/action/storage_attestation","attest_author":"https://pith.science/pith/3ONNUNHSQWFZ46RUIU46MOKG2S/action/author_attestation","sign_citation":"https://pith.science/pith/3ONNUNHSQWFZ46RUIU46MOKG2S/action/citation_signature","submit_replication":"https://pith.science/pith/3ONNUNHSQWFZ46RUIU46MOKG2S/action/replication_record"}},"created_at":"2026-05-18T00:19:56.489239+00:00","updated_at":"2026-05-18T00:19:56.489239+00:00"}