{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DEGZ5GL3U57EMHXX3P3HDLP3XB","short_pith_number":"pith:DEGZ5GL3","schema_version":"1.0","canonical_sha256":"190d9e997ba77e461ef7dbf671adfbb87f53e16f1fb22b19b8facffade02691b","source":{"kind":"arxiv","id":"2406.13895","version":2},"attestation_state":"computed","paper":{"title":"INFusion: Diffusion Regularized Implicit Neural Representations for 2D and 3D accelerated MRI reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.IV","authors_text":"Brett Levac, Jonathan Tamir, Yamin Arefeen, Zach Stoebner","submitted_at":"2024-06-19T23:51:26Z","abstract_excerpt":"Implicit Neural Representations (INRs) are a learning-based approach to accelerate Magnetic Resonance Imaging (MRI) acquisitions, particularly in scan-specific settings when only data from the under-sampled scan itself are available. Previous work demonstrates that INRs improve rapid MRI through inherent regularization imposed by neural network architectures. Typically parameterized by fully-connected neural networks, INRs support continuous image representations by taking a physical coordinate location as input and outputting the intensity at that coordinate. Previous work has applied unlearn"},"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":"2406.13895","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-06-19T23:51:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"6601e5582e184bdde34aebcb456ecc2cae29a6091c83d51b8415741593ef4996","abstract_canon_sha256":"f50a2e0c9db6e189950848569a05f27c2603dd1aa18ac7d0e9d8c30fec8da3a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:46:48.164128Z","signature_b64":"/tkmQ3UnoqN79APSQN6XiAz1VfOGia5CWuPDFkWEVE7JCj3Qj+VAEehBNrP/uzGkC+iJyaG55TofF8l/UJyEBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"190d9e997ba77e461ef7dbf671adfbb87f53e16f1fb22b19b8facffade02691b","last_reissued_at":"2026-07-05T09:46:48.163643Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:46:48.163643Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"INFusion: Diffusion Regularized Implicit Neural Representations for 2D and 3D accelerated MRI reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.IV","authors_text":"Brett Levac, Jonathan Tamir, Yamin Arefeen, Zach Stoebner","submitted_at":"2024-06-19T23:51:26Z","abstract_excerpt":"Implicit Neural Representations (INRs) are a learning-based approach to accelerate Magnetic Resonance Imaging (MRI) acquisitions, particularly in scan-specific settings when only data from the under-sampled scan itself are available. Previous work demonstrates that INRs improve rapid MRI through inherent regularization imposed by neural network architectures. Typically parameterized by fully-connected neural networks, INRs support continuous image representations by taking a physical coordinate location as input and outputting the intensity at that coordinate. Previous work has applied unlearn"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.13895","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/2406.13895/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":"2406.13895","created_at":"2026-07-05T09:46:48.163707+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.13895v2","created_at":"2026-07-05T09:46:48.163707+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.13895","created_at":"2026-07-05T09:46:48.163707+00:00"},{"alias_kind":"pith_short_12","alias_value":"DEGZ5GL3U57E","created_at":"2026-07-05T09:46:48.163707+00:00"},{"alias_kind":"pith_short_16","alias_value":"DEGZ5GL3U57EMHXX","created_at":"2026-07-05T09:46:48.163707+00:00"},{"alias_kind":"pith_short_8","alias_value":"DEGZ5GL3","created_at":"2026-07-05T09:46:48.163707+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.06043","citing_title":"Implicit Neural Representation-Based MRI Reconstruction Method with Sensitivity Map Constraints","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DEGZ5GL3U57EMHXX3P3HDLP3XB","json":"https://pith.science/pith/DEGZ5GL3U57EMHXX3P3HDLP3XB.json","graph_json":"https://pith.science/api/pith-number/DEGZ5GL3U57EMHXX3P3HDLP3XB/graph.json","events_json":"https://pith.science/api/pith-number/DEGZ5GL3U57EMHXX3P3HDLP3XB/events.json","paper":"https://pith.science/paper/DEGZ5GL3"},"agent_actions":{"view_html":"https://pith.science/pith/DEGZ5GL3U57EMHXX3P3HDLP3XB","download_json":"https://pith.science/pith/DEGZ5GL3U57EMHXX3P3HDLP3XB.json","view_paper":"https://pith.science/paper/DEGZ5GL3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.13895&json=true","fetch_graph":"https://pith.science/api/pith-number/DEGZ5GL3U57EMHXX3P3HDLP3XB/graph.json","fetch_events":"https://pith.science/api/pith-number/DEGZ5GL3U57EMHXX3P3HDLP3XB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DEGZ5GL3U57EMHXX3P3HDLP3XB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DEGZ5GL3U57EMHXX3P3HDLP3XB/action/storage_attestation","attest_author":"https://pith.science/pith/DEGZ5GL3U57EMHXX3P3HDLP3XB/action/author_attestation","sign_citation":"https://pith.science/pith/DEGZ5GL3U57EMHXX3P3HDLP3XB/action/citation_signature","submit_replication":"https://pith.science/pith/DEGZ5GL3U57EMHXX3P3HDLP3XB/action/replication_record"}},"created_at":"2026-07-05T09:46:48.163707+00:00","updated_at":"2026-07-05T09:46:48.163707+00:00"}