{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EJKUMD73YIWIBWTHG4NLLFF46X","short_pith_number":"pith:EJKUMD73","schema_version":"1.0","canonical_sha256":"2255460ffbc22c80da67371ab594bcf5ff8324077a7bfc2092cdea8567d76c04","source":{"kind":"arxiv","id":"2504.07560","version":1},"attestation_state":"computed","paper":{"title":"PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Fabian H\\\"orst, Helmut Becker, Jens Kleesiek, Kevin Kr\\\"oninger, Lukas Rotkopf, Marco Schlimbach, Moritz Rempe","submitted_at":"2025-04-10T08:44:19Z","abstract_excerpt":"Magnetic resonance imaging (MRI) raw data, or k-Space data, is complex-valued, containing both magnitude and phase information. However, clinical and existing Artificial Intelligence (AI)-based methods focus only on magnitude images, discarding the phase data despite its potential for downstream tasks, such as tumor segmentation and classification. In this work, we introduce $\\textit{PhaseGen}$, a novel complex-valued diffusion model for generating synthetic MRI raw data conditioned on magnitude images, commonly used in clinical practice. This enables the creation of artificial complex-valued "},"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":"2504.07560","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2025-04-10T08:44:19Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"dffa48c7abfce8132d85978520ae02d08bd93af8aa79ff48a1a802dc9f79a109","abstract_canon_sha256":"1a67e4039f1f3099ceec90cbcee12c062279483a66c221da4f7939a79887b3fa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:47:10.806806Z","signature_b64":"qrv11s6b1J7ye0NFAqtV9nw1SDzyky3ck5vdYjevfIv1QXpZx/CoitpG7glriyYLV3E+KEsVKKieRUPqm9VmAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2255460ffbc22c80da67371ab594bcf5ff8324077a7bfc2092cdea8567d76c04","last_reissued_at":"2026-07-05T10:47:10.806345Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:47:10.806345Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data Generation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Fabian H\\\"orst, Helmut Becker, Jens Kleesiek, Kevin Kr\\\"oninger, Lukas Rotkopf, Marco Schlimbach, Moritz Rempe","submitted_at":"2025-04-10T08:44:19Z","abstract_excerpt":"Magnetic resonance imaging (MRI) raw data, or k-Space data, is complex-valued, containing both magnitude and phase information. However, clinical and existing Artificial Intelligence (AI)-based methods focus only on magnitude images, discarding the phase data despite its potential for downstream tasks, such as tumor segmentation and classification. In this work, we introduce $\\textit{PhaseGen}$, a novel complex-valued diffusion model for generating synthetic MRI raw data conditioned on magnitude images, commonly used in clinical practice. This enables the creation of artificial complex-valued "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.07560","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/2504.07560/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":"2504.07560","created_at":"2026-07-05T10:47:10.806404+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.07560v1","created_at":"2026-07-05T10:47:10.806404+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.07560","created_at":"2026-07-05T10:47:10.806404+00:00"},{"alias_kind":"pith_short_12","alias_value":"EJKUMD73YIWI","created_at":"2026-07-05T10:47:10.806404+00:00"},{"alias_kind":"pith_short_16","alias_value":"EJKUMD73YIWIBWTH","created_at":"2026-07-05T10:47:10.806404+00:00"},{"alias_kind":"pith_short_8","alias_value":"EJKUMD73","created_at":"2026-07-05T10:47:10.806404+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.14800","citing_title":"Generative Modeling of Complex-Valued Brain MRI Data","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EJKUMD73YIWIBWTHG4NLLFF46X","json":"https://pith.science/pith/EJKUMD73YIWIBWTHG4NLLFF46X.json","graph_json":"https://pith.science/api/pith-number/EJKUMD73YIWIBWTHG4NLLFF46X/graph.json","events_json":"https://pith.science/api/pith-number/EJKUMD73YIWIBWTHG4NLLFF46X/events.json","paper":"https://pith.science/paper/EJKUMD73"},"agent_actions":{"view_html":"https://pith.science/pith/EJKUMD73YIWIBWTHG4NLLFF46X","download_json":"https://pith.science/pith/EJKUMD73YIWIBWTHG4NLLFF46X.json","view_paper":"https://pith.science/paper/EJKUMD73","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.07560&json=true","fetch_graph":"https://pith.science/api/pith-number/EJKUMD73YIWIBWTHG4NLLFF46X/graph.json","fetch_events":"https://pith.science/api/pith-number/EJKUMD73YIWIBWTHG4NLLFF46X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EJKUMD73YIWIBWTHG4NLLFF46X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EJKUMD73YIWIBWTHG4NLLFF46X/action/storage_attestation","attest_author":"https://pith.science/pith/EJKUMD73YIWIBWTHG4NLLFF46X/action/author_attestation","sign_citation":"https://pith.science/pith/EJKUMD73YIWIBWTHG4NLLFF46X/action/citation_signature","submit_replication":"https://pith.science/pith/EJKUMD73YIWIBWTHG4NLLFF46X/action/replication_record"}},"created_at":"2026-07-05T10:47:10.806404+00:00","updated_at":"2026-07-05T10:47:10.806404+00:00"}