{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZDPQTBEAASD3U54IGI6YDLZ5OS","short_pith_number":"pith:ZDPQTBEA","schema_version":"1.0","canonical_sha256":"c8df0984800487ba7788323d81af3d749ae98956a4d7c5eb797f37506977d3d9","source":{"kind":"arxiv","id":"2404.04785","version":1},"attestation_state":"computed","paper":{"title":"Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Rao, Guangyuan Li, Juncheng Mo, Lei Zhao, Wei Xing, Zhanjie Zhang","submitted_at":"2024-04-07T02:15:43Z","abstract_excerpt":"Recently, diffusion models (DM) have been applied in magnetic resonance imaging (MRI) super-resolution (SR) reconstruction, exhibiting impressive performance, especially with regard to detailed reconstruction. However, the current DM-based SR reconstruction methods still face the following issues: (1) They require a large number of iterations to reconstruct the final image, which is inefficient and consumes a significant amount of computational resources. (2) The results reconstructed by these methods are often misaligned with the real high-resolution images, leading to remarkable distortion i"},"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":"2404.04785","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-04-07T02:15:43Z","cross_cats_sorted":[],"title_canon_sha256":"cb7528427301bcbc1d79a0e3babc56182048a2bf7f026e59fd1fe30726554701","abstract_canon_sha256":"f557a7f9f6dd4ffa443c7dd6580e87a045c1d38913e04ab2f00d1eaf53415940"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:05:21.642666Z","signature_b64":"5Erqa2vJHMlEBx9biUyhQ2DtMeV8GrK5VXO7MZMuzLLUEHOhkGMJJsZwihFIV6qQmLaUbcTu9ozLfk3SPKlSCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c8df0984800487ba7788323d81af3d749ae98956a4d7c5eb797f37506977d3d9","last_reissued_at":"2026-07-05T08:05:21.642179Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:05:21.642179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Rao, Guangyuan Li, Juncheng Mo, Lei Zhao, Wei Xing, Zhanjie Zhang","submitted_at":"2024-04-07T02:15:43Z","abstract_excerpt":"Recently, diffusion models (DM) have been applied in magnetic resonance imaging (MRI) super-resolution (SR) reconstruction, exhibiting impressive performance, especially with regard to detailed reconstruction. However, the current DM-based SR reconstruction methods still face the following issues: (1) They require a large number of iterations to reconstruct the final image, which is inefficient and consumes a significant amount of computational resources. (2) The results reconstructed by these methods are often misaligned with the real high-resolution images, leading to remarkable distortion i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.04785","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/2404.04785/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":"2404.04785","created_at":"2026-07-05T08:05:21.642241+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.04785v1","created_at":"2026-07-05T08:05:21.642241+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.04785","created_at":"2026-07-05T08:05:21.642241+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZDPQTBEAASD3","created_at":"2026-07-05T08:05:21.642241+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZDPQTBEAASD3U54I","created_at":"2026-07-05T08:05:21.642241+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZDPQTBEA","created_at":"2026-07-05T08:05:21.642241+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/ZDPQTBEAASD3U54IGI6YDLZ5OS","json":"https://pith.science/pith/ZDPQTBEAASD3U54IGI6YDLZ5OS.json","graph_json":"https://pith.science/api/pith-number/ZDPQTBEAASD3U54IGI6YDLZ5OS/graph.json","events_json":"https://pith.science/api/pith-number/ZDPQTBEAASD3U54IGI6YDLZ5OS/events.json","paper":"https://pith.science/paper/ZDPQTBEA"},"agent_actions":{"view_html":"https://pith.science/pith/ZDPQTBEAASD3U54IGI6YDLZ5OS","download_json":"https://pith.science/pith/ZDPQTBEAASD3U54IGI6YDLZ5OS.json","view_paper":"https://pith.science/paper/ZDPQTBEA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.04785&json=true","fetch_graph":"https://pith.science/api/pith-number/ZDPQTBEAASD3U54IGI6YDLZ5OS/graph.json","fetch_events":"https://pith.science/api/pith-number/ZDPQTBEAASD3U54IGI6YDLZ5OS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZDPQTBEAASD3U54IGI6YDLZ5OS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZDPQTBEAASD3U54IGI6YDLZ5OS/action/storage_attestation","attest_author":"https://pith.science/pith/ZDPQTBEAASD3U54IGI6YDLZ5OS/action/author_attestation","sign_citation":"https://pith.science/pith/ZDPQTBEAASD3U54IGI6YDLZ5OS/action/citation_signature","submit_replication":"https://pith.science/pith/ZDPQTBEAASD3U54IGI6YDLZ5OS/action/replication_record"}},"created_at":"2026-07-05T08:05:21.642241+00:00","updated_at":"2026-07-05T08:05:21.642241+00:00"}