{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YXUYM7ZQTKRYOOGJTRXQDP4EHG","short_pith_number":"pith:YXUYM7ZQ","schema_version":"1.0","canonical_sha256":"c5e9867f309aa38738c99c6f01bf8439894c2ee82ecabb1bfd9d3e537a6200ce","source":{"kind":"arxiv","id":"2404.03706","version":1},"attestation_state":"computed","paper":{"title":"Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.IV","authors_text":"Fred Roosta, Hongfu Sun, Hossein Askari","submitted_at":"2024-04-04T10:36:56Z","abstract_excerpt":"In the realm of medical imaging, inverse problems aim to infer high-quality images from incomplete, noisy measurements, with the objective of minimizing expenses and risks to patients in clinical settings. The Diffusion Models have recently emerged as a promising approach to such practical challenges, proving particularly useful for the zero-shot inference of images from partially acquired measurements in Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). A central challenge in this approach, however, is how to guide an unconditional prediction to conform to the measurement informa"},"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":"2404.03706","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-04-04T10:36:56Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4f9909f27b1620da613c3bf8a1343be8ce5f75d05d78e3d18a5774c6334c40de","abstract_canon_sha256":"fb7e207203b108152c78c1fc36c31baf3c0beb343e78ca924d57ebaeb7faf733"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:04:40.482161Z","signature_b64":"lr/gtzVUIMWXv1iHEuhE4HdmYQDaPsoBkRc0guVBD4aWpn406lG/NrQiGWesJIyK8F67oRb7P8pJHeJzxd1XAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5e9867f309aa38738c99c6f01bf8439894c2ee82ecabb1bfd9d3e537a6200ce","last_reissued_at":"2026-07-05T08:04:40.480840Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:04:40.480840Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bi-level Guided Diffusion Models for Zero-Shot Medical Imaging Inverse Problems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.IV","authors_text":"Fred Roosta, Hongfu Sun, Hossein Askari","submitted_at":"2024-04-04T10:36:56Z","abstract_excerpt":"In the realm of medical imaging, inverse problems aim to infer high-quality images from incomplete, noisy measurements, with the objective of minimizing expenses and risks to patients in clinical settings. The Diffusion Models have recently emerged as a promising approach to such practical challenges, proving particularly useful for the zero-shot inference of images from partially acquired measurements in Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). A central challenge in this approach, however, is how to guide an unconditional prediction to conform to the measurement informa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.03706","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/2404.03706/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":"2404.03706","created_at":"2026-07-05T08:04:40.480893+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.03706v1","created_at":"2026-07-05T08:04:40.480893+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.03706","created_at":"2026-07-05T08:04:40.480893+00:00"},{"alias_kind":"pith_short_12","alias_value":"YXUYM7ZQTKRY","created_at":"2026-07-05T08:04:40.480893+00:00"},{"alias_kind":"pith_short_16","alias_value":"YXUYM7ZQTKRYOOGJ","created_at":"2026-07-05T08:04:40.480893+00:00"},{"alias_kind":"pith_short_8","alias_value":"YXUYM7ZQ","created_at":"2026-07-05T08:04:40.480893+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.15309","citing_title":"Investigating the Feasibility of Patch-based Inference for Generalized Diffusion Priors in Inverse Problems for Medical Images","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YXUYM7ZQTKRYOOGJTRXQDP4EHG","json":"https://pith.science/pith/YXUYM7ZQTKRYOOGJTRXQDP4EHG.json","graph_json":"https://pith.science/api/pith-number/YXUYM7ZQTKRYOOGJTRXQDP4EHG/graph.json","events_json":"https://pith.science/api/pith-number/YXUYM7ZQTKRYOOGJTRXQDP4EHG/events.json","paper":"https://pith.science/paper/YXUYM7ZQ"},"agent_actions":{"view_html":"https://pith.science/pith/YXUYM7ZQTKRYOOGJTRXQDP4EHG","download_json":"https://pith.science/pith/YXUYM7ZQTKRYOOGJTRXQDP4EHG.json","view_paper":"https://pith.science/paper/YXUYM7ZQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.03706&json=true","fetch_graph":"https://pith.science/api/pith-number/YXUYM7ZQTKRYOOGJTRXQDP4EHG/graph.json","fetch_events":"https://pith.science/api/pith-number/YXUYM7ZQTKRYOOGJTRXQDP4EHG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YXUYM7ZQTKRYOOGJTRXQDP4EHG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YXUYM7ZQTKRYOOGJTRXQDP4EHG/action/storage_attestation","attest_author":"https://pith.science/pith/YXUYM7ZQTKRYOOGJTRXQDP4EHG/action/author_attestation","sign_citation":"https://pith.science/pith/YXUYM7ZQTKRYOOGJTRXQDP4EHG/action/citation_signature","submit_replication":"https://pith.science/pith/YXUYM7ZQTKRYOOGJTRXQDP4EHG/action/replication_record"}},"created_at":"2026-07-05T08:04:40.480893+00:00","updated_at":"2026-07-05T08:04:40.480893+00:00"}