{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HMDOATBNXOGU2AS5BJIQ54BNZ6","short_pith_number":"pith:HMDOATBN","schema_version":"1.0","canonical_sha256":"3b06e04c2dbb8d4d025d0a510ef02dcfa90de87060762f772ef19db64d64ac72","source":{"kind":"arxiv","id":"2411.03758","version":1},"attestation_state":"computed","paper":{"title":"Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Dong Liang, Qiegen Liu, Qinrong Cai, Qiuyun Fan, Wei Li, Yu Guan","submitted_at":"2024-11-06T08:33:07Z","abstract_excerpt":"Diffusion model-based approaches recently achieved re-markable success in MRI reconstruction, but integration into clinical routine remains challenging due to its time-consuming convergence. This phenomenon is partic-ularly notable when directly apply conventional diffusion process to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. To tackle these challenges, we introduce subspace diffusion model with orthogonal decomposition, a method (referred to as Sub-DM) that restrict the diffusion process"},"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":"2411.03758","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-11-06T08:33:07Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"acbcc6d13e0602f959e5ed858c8e260808a37e47f89c0affa29724c72706d5e3","abstract_canon_sha256":"cb5b053f214ea9b42533d8a7ba7ab52cf209eb2ff79a496dfdc3b6a68e605c6b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:31:55.435118Z","signature_b64":"AhvNpdZqlUfR/YBNUvSvuv2aqjlkN19xQAB7/NxS5oljsk3uDetr0sBEYTLfIssvHbDYMZuIJKAMGU0SItzvBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3b06e04c2dbb8d4d025d0a510ef02dcfa90de87060762f772ef19db64d64ac72","last_reissued_at":"2026-07-05T09:31:55.434659Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:31:55.434659Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sub-DM:Subspace Diffusion Model with Orthogonal Decomposition for MRI Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Dong Liang, Qiegen Liu, Qinrong Cai, Qiuyun Fan, Wei Li, Yu Guan","submitted_at":"2024-11-06T08:33:07Z","abstract_excerpt":"Diffusion model-based approaches recently achieved re-markable success in MRI reconstruction, but integration into clinical routine remains challenging due to its time-consuming convergence. This phenomenon is partic-ularly notable when directly apply conventional diffusion process to k-space data without considering the inherent properties of k-space sampling, limiting k-space learning efficiency and image reconstruction quality. To tackle these challenges, we introduce subspace diffusion model with orthogonal decomposition, a method (referred to as Sub-DM) that restrict the diffusion process"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.03758","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/2411.03758/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":"2411.03758","created_at":"2026-07-05T09:31:55.434727+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.03758v1","created_at":"2026-07-05T09:31:55.434727+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.03758","created_at":"2026-07-05T09:31:55.434727+00:00"},{"alias_kind":"pith_short_12","alias_value":"HMDOATBNXOGU","created_at":"2026-07-05T09:31:55.434727+00:00"},{"alias_kind":"pith_short_16","alias_value":"HMDOATBNXOGU2AS5","created_at":"2026-07-05T09:31:55.434727+00:00"},{"alias_kind":"pith_short_8","alias_value":"HMDOATBN","created_at":"2026-07-05T09:31:55.434727+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.18270","citing_title":"Adaptive Mask-guided K-space Diffusion for Accelerated MRI Reconstruction","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HMDOATBNXOGU2AS5BJIQ54BNZ6","json":"https://pith.science/pith/HMDOATBNXOGU2AS5BJIQ54BNZ6.json","graph_json":"https://pith.science/api/pith-number/HMDOATBNXOGU2AS5BJIQ54BNZ6/graph.json","events_json":"https://pith.science/api/pith-number/HMDOATBNXOGU2AS5BJIQ54BNZ6/events.json","paper":"https://pith.science/paper/HMDOATBN"},"agent_actions":{"view_html":"https://pith.science/pith/HMDOATBNXOGU2AS5BJIQ54BNZ6","download_json":"https://pith.science/pith/HMDOATBNXOGU2AS5BJIQ54BNZ6.json","view_paper":"https://pith.science/paper/HMDOATBN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.03758&json=true","fetch_graph":"https://pith.science/api/pith-number/HMDOATBNXOGU2AS5BJIQ54BNZ6/graph.json","fetch_events":"https://pith.science/api/pith-number/HMDOATBNXOGU2AS5BJIQ54BNZ6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HMDOATBNXOGU2AS5BJIQ54BNZ6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HMDOATBNXOGU2AS5BJIQ54BNZ6/action/storage_attestation","attest_author":"https://pith.science/pith/HMDOATBNXOGU2AS5BJIQ54BNZ6/action/author_attestation","sign_citation":"https://pith.science/pith/HMDOATBNXOGU2AS5BJIQ54BNZ6/action/citation_signature","submit_replication":"https://pith.science/pith/HMDOATBNXOGU2AS5BJIQ54BNZ6/action/replication_record"}},"created_at":"2026-07-05T09:31:55.434727+00:00","updated_at":"2026-07-05T09:31:55.434727+00:00"}