{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WSIRJM7P2YUMSJDYQ2AAW3NBS6","short_pith_number":"pith:WSIRJM7P","schema_version":"1.0","canonical_sha256":"b49114b3efd628c9247886800b6da19787a1c36ac850d24717b7d63289a3304f","source":{"kind":"arxiv","id":"2605.29753","version":1},"attestation_state":"computed","paper":{"title":"A unified deeplearning framework for contrast-phase-specific virtual monochromatic imaging","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"eess.IV","authors_text":"Antony Jerald, Avinash Gopal, Bipul Das, Brian Nett, Hemant K Aggarwal, Phaneendra K Yalavarthy, Rajesh Langoju","submitted_at":"2026-05-28T10:55:28Z","abstract_excerpt":"Dual-energy CT (DECT) enables virtual monochromatic imaging (VMI) and improved contrast resolution, but its clinical adoption is limited by hardware complexity and cost. In this work, we propose a unified deep learning framework that synthesizes contrast-phase-specific virtual monochromatic 50 keV images from single-energy CT (SECT) data by leveraging contrast phase information as a prior. The model is trained using DECT-derived 70 keV and 50 keV image pairs across four contrast phases -- Angio, Arterial, Portal, and Delayed -- using a novel prior conditioning architecture that integrates cont"},"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":"2605.29753","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2026-05-28T10:55:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b36351d83b4bc8e91f2aa8186c3f0953e31d35139cf1c83ecdec047a1ffff0f0","abstract_canon_sha256":"bfb280d8cc12d822b90f4f0b7513e7e57d22693cd3fdef2ace7fd0b814d4ee1c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:05:57.891004Z","signature_b64":"r8DZv7wR+B3OuUZz8/B3kQ+LdZz2eKifxO1AlmF+DZz3ldOAK1oN8HoYu6ssC8Gkk82wADsX3aquczR4yE0PBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b49114b3efd628c9247886800b6da19787a1c36ac850d24717b7d63289a3304f","last_reissued_at":"2026-05-29T01:05:57.890561Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:05:57.890561Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A unified deeplearning framework for contrast-phase-specific virtual monochromatic imaging","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"eess.IV","authors_text":"Antony Jerald, Avinash Gopal, Bipul Das, Brian Nett, Hemant K Aggarwal, Phaneendra K Yalavarthy, Rajesh Langoju","submitted_at":"2026-05-28T10:55:28Z","abstract_excerpt":"Dual-energy CT (DECT) enables virtual monochromatic imaging (VMI) and improved contrast resolution, but its clinical adoption is limited by hardware complexity and cost. In this work, we propose a unified deep learning framework that synthesizes contrast-phase-specific virtual monochromatic 50 keV images from single-energy CT (SECT) data by leveraging contrast phase information as a prior. The model is trained using DECT-derived 70 keV and 50 keV image pairs across four contrast phases -- Angio, Arterial, Portal, and Delayed -- using a novel prior conditioning architecture that integrates cont"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29753","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/2605.29753/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":"2605.29753","created_at":"2026-05-29T01:05:57.890629+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.29753v1","created_at":"2026-05-29T01:05:57.890629+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29753","created_at":"2026-05-29T01:05:57.890629+00:00"},{"alias_kind":"pith_short_12","alias_value":"WSIRJM7P2YUM","created_at":"2026-05-29T01:05:57.890629+00:00"},{"alias_kind":"pith_short_16","alias_value":"WSIRJM7P2YUMSJDY","created_at":"2026-05-29T01:05:57.890629+00:00"},{"alias_kind":"pith_short_8","alias_value":"WSIRJM7P","created_at":"2026-05-29T01:05:57.890629+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/WSIRJM7P2YUMSJDYQ2AAW3NBS6","json":"https://pith.science/pith/WSIRJM7P2YUMSJDYQ2AAW3NBS6.json","graph_json":"https://pith.science/api/pith-number/WSIRJM7P2YUMSJDYQ2AAW3NBS6/graph.json","events_json":"https://pith.science/api/pith-number/WSIRJM7P2YUMSJDYQ2AAW3NBS6/events.json","paper":"https://pith.science/paper/WSIRJM7P"},"agent_actions":{"view_html":"https://pith.science/pith/WSIRJM7P2YUMSJDYQ2AAW3NBS6","download_json":"https://pith.science/pith/WSIRJM7P2YUMSJDYQ2AAW3NBS6.json","view_paper":"https://pith.science/paper/WSIRJM7P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.29753&json=true","fetch_graph":"https://pith.science/api/pith-number/WSIRJM7P2YUMSJDYQ2AAW3NBS6/graph.json","fetch_events":"https://pith.science/api/pith-number/WSIRJM7P2YUMSJDYQ2AAW3NBS6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WSIRJM7P2YUMSJDYQ2AAW3NBS6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WSIRJM7P2YUMSJDYQ2AAW3NBS6/action/storage_attestation","attest_author":"https://pith.science/pith/WSIRJM7P2YUMSJDYQ2AAW3NBS6/action/author_attestation","sign_citation":"https://pith.science/pith/WSIRJM7P2YUMSJDYQ2AAW3NBS6/action/citation_signature","submit_replication":"https://pith.science/pith/WSIRJM7P2YUMSJDYQ2AAW3NBS6/action/replication_record"}},"created_at":"2026-05-29T01:05:57.890629+00:00","updated_at":"2026-05-29T01:05:57.890629+00:00"}