{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IUAN3HCCEPHQFSRCW74D3HCEET","short_pith_number":"pith:IUAN3HCC","schema_version":"1.0","canonical_sha256":"4500dd9c4223cf02ca22b7f83d9c4424cd0ca237d94ff9c0eb0123f09109e2f3","source":{"kind":"arxiv","id":"2405.03684","version":2},"attestation_state":"computed","paper":{"title":"All-in-One Deep Learning Framework for MR Image Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Geunu Jeong, Hyeonsoo Kim, Jeewook Kim, Joonyoung Yang, Kyungeun Jang","submitted_at":"2024-05-06T17:57:06Z","abstract_excerpt":"We introduce a novel, all-in-one deep learning framework for MR image reconstruction, enabling a single model to enhance image quality across multiple aspects of k-space sampling and to be effective across a wide range of clinical and technical scenarios. This DICOM-based algorithm serves as the core of SwiftMR (AIRS Medical, Seoul, Korea), which is FDA-cleared, CE-certified, and commercially available. We first detail the comprehensive development process of the model, including data collection, training pair preparation, model architecture design, and DICOM inference. We then assess the mode"},"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":"2405.03684","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-05-06T17:57:06Z","cross_cats_sorted":[],"title_canon_sha256":"0e1f4194710833c587175293dfdf8fffc9b1567b6e2800682abd6467e2fcf91b","abstract_canon_sha256":"ea6370772b8ea96ad9597a36328d9c2fe8aef66aac004edc9207679644e05316"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:23:22.393892Z","signature_b64":"clQC8SJ8rRFIyVUXo07ZMvUCZNyTJOjcsBEmSnU1fu+E1jtCLjyT9zEq+h6h39PB09Nx9cteVJz1kwLETAmiCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4500dd9c4223cf02ca22b7f83d9c4424cd0ca237d94ff9c0eb0123f09109e2f3","last_reissued_at":"2026-07-05T08:23:22.393426Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:23:22.393426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"All-in-One Deep Learning Framework for MR Image Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Geunu Jeong, Hyeonsoo Kim, Jeewook Kim, Joonyoung Yang, Kyungeun Jang","submitted_at":"2024-05-06T17:57:06Z","abstract_excerpt":"We introduce a novel, all-in-one deep learning framework for MR image reconstruction, enabling a single model to enhance image quality across multiple aspects of k-space sampling and to be effective across a wide range of clinical and technical scenarios. This DICOM-based algorithm serves as the core of SwiftMR (AIRS Medical, Seoul, Korea), which is FDA-cleared, CE-certified, and commercially available. We first detail the comprehensive development process of the model, including data collection, training pair preparation, model architecture design, and DICOM inference. We then assess the mode"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.03684","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2405.03684/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":"2405.03684","created_at":"2026-07-05T08:23:22.393497+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.03684v2","created_at":"2026-07-05T08:23:22.393497+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.03684","created_at":"2026-07-05T08:23:22.393497+00:00"},{"alias_kind":"pith_short_12","alias_value":"IUAN3HCCEPHQ","created_at":"2026-07-05T08:23:22.393497+00:00"},{"alias_kind":"pith_short_16","alias_value":"IUAN3HCCEPHQFSRC","created_at":"2026-07-05T08:23:22.393497+00:00"},{"alias_kind":"pith_short_8","alias_value":"IUAN3HCC","created_at":"2026-07-05T08:23:22.393497+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/IUAN3HCCEPHQFSRCW74D3HCEET","json":"https://pith.science/pith/IUAN3HCCEPHQFSRCW74D3HCEET.json","graph_json":"https://pith.science/api/pith-number/IUAN3HCCEPHQFSRCW74D3HCEET/graph.json","events_json":"https://pith.science/api/pith-number/IUAN3HCCEPHQFSRCW74D3HCEET/events.json","paper":"https://pith.science/paper/IUAN3HCC"},"agent_actions":{"view_html":"https://pith.science/pith/IUAN3HCCEPHQFSRCW74D3HCEET","download_json":"https://pith.science/pith/IUAN3HCCEPHQFSRCW74D3HCEET.json","view_paper":"https://pith.science/paper/IUAN3HCC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.03684&json=true","fetch_graph":"https://pith.science/api/pith-number/IUAN3HCCEPHQFSRCW74D3HCEET/graph.json","fetch_events":"https://pith.science/api/pith-number/IUAN3HCCEPHQFSRCW74D3HCEET/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IUAN3HCCEPHQFSRCW74D3HCEET/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IUAN3HCCEPHQFSRCW74D3HCEET/action/storage_attestation","attest_author":"https://pith.science/pith/IUAN3HCCEPHQFSRCW74D3HCEET/action/author_attestation","sign_citation":"https://pith.science/pith/IUAN3HCCEPHQFSRCW74D3HCEET/action/citation_signature","submit_replication":"https://pith.science/pith/IUAN3HCCEPHQFSRCW74D3HCEET/action/replication_record"}},"created_at":"2026-07-05T08:23:22.393497+00:00","updated_at":"2026-07-05T08:23:22.393497+00:00"}