{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:4HRQVBE35IWNUB6AH2C27LNWBD","short_pith_number":"pith:4HRQVBE3","schema_version":"1.0","canonical_sha256":"e1e30a849bea2cda07c03e85afadb608f432f157d0df4218690d858daa4fff9c","source":{"kind":"arxiv","id":"1904.05789","version":1},"attestation_state":"computed","paper":{"title":"MRI-only brain radiotherapy: assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.med-ph","authors_text":"Amir Owrangi, Dan Nguyen, Robert Timmerman, Samaneh Kazemifar, Sarah McGuire, Steve Jiang, Yang Park, Zabi Wardak","submitted_at":"2019-04-11T15:44:46Z","abstract_excerpt":"Purpose: This study assessed the dosimetric accuracy of synthetic CT images generated from magnetic resonance imaging (MRI) data for focal brain radiation therapy, using a deep learning approach. Material and Methods: We conducted a study in 77 patients with brain tumors who had undergone both MRI and computed tomography (CT) imaging as part of their simulation for external beam treatment planning. We designed a generative adversarial network (GAN) to generate synthetic CT images from MRI images. We used Mutual Information (MI) as the loss function in the generator to overcome the misalignment"},"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":"1904.05789","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2019-04-11T15:44:46Z","cross_cats_sorted":[],"title_canon_sha256":"bc591f7d46a5bbd154710658d15b91ff1e4b320a67f52efa81523c70a12d0904","abstract_canon_sha256":"792c81b54e7e151f6438afc584601310864b49614d2b82f99dda06b25bad7f6b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:48.434785Z","signature_b64":"K9gjaSuH41cifau2VryOGSkxYXNTciF2S7cJEC78MKqvTl4aAw8fGgzMMYKtaTHDYMQ5wE5SRYipL8T0zBY9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1e30a849bea2cda07c03e85afadb608f432f157d0df4218690d858daa4fff9c","last_reissued_at":"2026-05-17T23:48:48.434340Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:48.434340Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MRI-only brain radiotherapy: assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.med-ph","authors_text":"Amir Owrangi, Dan Nguyen, Robert Timmerman, Samaneh Kazemifar, Sarah McGuire, Steve Jiang, Yang Park, Zabi Wardak","submitted_at":"2019-04-11T15:44:46Z","abstract_excerpt":"Purpose: This study assessed the dosimetric accuracy of synthetic CT images generated from magnetic resonance imaging (MRI) data for focal brain radiation therapy, using a deep learning approach. Material and Methods: We conducted a study in 77 patients with brain tumors who had undergone both MRI and computed tomography (CT) imaging as part of their simulation for external beam treatment planning. We designed a generative adversarial network (GAN) to generate synthetic CT images from MRI images. We used Mutual Information (MI) as the loss function in the generator to overcome the misalignment"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.05789","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":""},"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":"1904.05789","created_at":"2026-05-17T23:48:48.434402+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.05789v1","created_at":"2026-05-17T23:48:48.434402+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.05789","created_at":"2026-05-17T23:48:48.434402+00:00"},{"alias_kind":"pith_short_12","alias_value":"4HRQVBE35IWN","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"4HRQVBE35IWNUB6A","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"4HRQVBE3","created_at":"2026-05-18T12:33:10.108867+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/4HRQVBE35IWNUB6AH2C27LNWBD","json":"https://pith.science/pith/4HRQVBE35IWNUB6AH2C27LNWBD.json","graph_json":"https://pith.science/api/pith-number/4HRQVBE35IWNUB6AH2C27LNWBD/graph.json","events_json":"https://pith.science/api/pith-number/4HRQVBE35IWNUB6AH2C27LNWBD/events.json","paper":"https://pith.science/paper/4HRQVBE3"},"agent_actions":{"view_html":"https://pith.science/pith/4HRQVBE35IWNUB6AH2C27LNWBD","download_json":"https://pith.science/pith/4HRQVBE35IWNUB6AH2C27LNWBD.json","view_paper":"https://pith.science/paper/4HRQVBE3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.05789&json=true","fetch_graph":"https://pith.science/api/pith-number/4HRQVBE35IWNUB6AH2C27LNWBD/graph.json","fetch_events":"https://pith.science/api/pith-number/4HRQVBE35IWNUB6AH2C27LNWBD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4HRQVBE35IWNUB6AH2C27LNWBD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4HRQVBE35IWNUB6AH2C27LNWBD/action/storage_attestation","attest_author":"https://pith.science/pith/4HRQVBE35IWNUB6AH2C27LNWBD/action/author_attestation","sign_citation":"https://pith.science/pith/4HRQVBE35IWNUB6AH2C27LNWBD/action/citation_signature","submit_replication":"https://pith.science/pith/4HRQVBE35IWNUB6AH2C27LNWBD/action/replication_record"}},"created_at":"2026-05-17T23:48:48.434402+00:00","updated_at":"2026-05-17T23:48:48.434402+00:00"}