{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:Q4RWIUKDE3CFSKG65DTSK53CTB","short_pith_number":"pith:Q4RWIUKD","schema_version":"1.0","canonical_sha256":"872364514326c45928dee8e72577629848faa5ae0518815e949a06f34ef0b3da","source":{"kind":"arxiv","id":"2607.01176","version":1},"attestation_state":"computed","paper":{"title":"High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dong Liang, Qiegen Liu, Qinrong Cai, Qiuyun Fan, Tianjia Huang, Yu Guan","submitted_at":"2026-07-01T17:08:17Z","abstract_excerpt":"Magnetic resonance imaging (MRI) reconstruction under realistic acquisition conditions can be fundamentally viewed as estimating the underlying k-space distribution from incomplete and noise-corrupted measurements. While diffusion models have recently shown strong potential as generative prior for inverse problems,existingapproachesstruggletohandlenoisyreconstruction settings, especially when operating directly in k-space domain. In this work, we propose a unified high-dimensional k-space reconstruction framework tailored for noisy inverse problems, whichenhancesdiffusion-based solversthroughr"},"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":"2607.01176","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-01T17:08:17Z","cross_cats_sorted":[],"title_canon_sha256":"ddd1006522b91e9c847d46a64dc0a5aa859e9ed21b2f34ac96877c55b61d2fdc","abstract_canon_sha256":"ddd16252ba705c6e1b5a6c2d89b9a949e98055e9dacfb7f3cd2395fbcbf971e9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:18:31.774513Z","signature_b64":"Sn1NwMOogTmD6XaX465RZgBhg1BRP18kal/ymsPABRCOyTD4S8xdaKwnrDaXSMlyn1RvaMljdSg6f9l1EM9KBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"872364514326c45928dee8e72577629848faa5ae0518815e949a06f34ef0b3da","last_reissued_at":"2026-07-02T01:18:31.774082Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:18:31.774082Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"High-dimensional Embedding Prior for Noisy K-space Domain MRIReconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dong Liang, Qiegen Liu, Qinrong Cai, Qiuyun Fan, Tianjia Huang, Yu Guan","submitted_at":"2026-07-01T17:08:17Z","abstract_excerpt":"Magnetic resonance imaging (MRI) reconstruction under realistic acquisition conditions can be fundamentally viewed as estimating the underlying k-space distribution from incomplete and noise-corrupted measurements. While diffusion models have recently shown strong potential as generative prior for inverse problems,existingapproachesstruggletohandlenoisyreconstruction settings, especially when operating directly in k-space domain. In this work, we propose a unified high-dimensional k-space reconstruction framework tailored for noisy inverse problems, whichenhancesdiffusion-based solversthroughr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.01176","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/2607.01176/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":"2607.01176","created_at":"2026-07-02T01:18:31.774143+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.01176v1","created_at":"2026-07-02T01:18:31.774143+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.01176","created_at":"2026-07-02T01:18:31.774143+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q4RWIUKDE3CF","created_at":"2026-07-02T01:18:31.774143+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q4RWIUKDE3CFSKG6","created_at":"2026-07-02T01:18:31.774143+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q4RWIUKD","created_at":"2026-07-02T01:18:31.774143+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/Q4RWIUKDE3CFSKG65DTSK53CTB","json":"https://pith.science/pith/Q4RWIUKDE3CFSKG65DTSK53CTB.json","graph_json":"https://pith.science/api/pith-number/Q4RWIUKDE3CFSKG65DTSK53CTB/graph.json","events_json":"https://pith.science/api/pith-number/Q4RWIUKDE3CFSKG65DTSK53CTB/events.json","paper":"https://pith.science/paper/Q4RWIUKD"},"agent_actions":{"view_html":"https://pith.science/pith/Q4RWIUKDE3CFSKG65DTSK53CTB","download_json":"https://pith.science/pith/Q4RWIUKDE3CFSKG65DTSK53CTB.json","view_paper":"https://pith.science/paper/Q4RWIUKD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.01176&json=true","fetch_graph":"https://pith.science/api/pith-number/Q4RWIUKDE3CFSKG65DTSK53CTB/graph.json","fetch_events":"https://pith.science/api/pith-number/Q4RWIUKDE3CFSKG65DTSK53CTB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q4RWIUKDE3CFSKG65DTSK53CTB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q4RWIUKDE3CFSKG65DTSK53CTB/action/storage_attestation","attest_author":"https://pith.science/pith/Q4RWIUKDE3CFSKG65DTSK53CTB/action/author_attestation","sign_citation":"https://pith.science/pith/Q4RWIUKDE3CFSKG65DTSK53CTB/action/citation_signature","submit_replication":"https://pith.science/pith/Q4RWIUKDE3CFSKG65DTSK53CTB/action/replication_record"}},"created_at":"2026-07-02T01:18:31.774143+00:00","updated_at":"2026-07-02T01:18:31.774143+00:00"}