{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:II23TVHB64YGYTP4VRFD33IPAJ","short_pith_number":"pith:II23TVHB","schema_version":"1.0","canonical_sha256":"4235b9d4e1f7306c4dfcac4a3ded0f02754038e36142bf9a7c521c21f5deff4d","source":{"kind":"arxiv","id":"2501.07376","version":1},"attestation_state":"computed","paper":{"title":"Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Lukas Glaszner, Martin Zach","submitted_at":"2025-01-13T14:51:15Z","abstract_excerpt":"Diffusion model have been successfully applied to many inverse problems, including MRI and CT reconstruction. Researchers typically re-purpose models originally designed for unconditional sampling without modifications. Using two different posterior sampling algorithms, we show empirically that such large networks are not necessary. Our smallest model, effectively a ResNet, performs almost as good as an attention U-Net on in-distribution reconstruction, while being significantly more robust towards distribution shifts. Furthermore, we introduce models trained on natural images and demonstrate "},"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":"2501.07376","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2025-01-13T14:51:15Z","cross_cats_sorted":[],"title_canon_sha256":"aef6e901be8190772e74d9bb4331aa011b378bab9cffd3e82a711267f17d6e1d","abstract_canon_sha256":"276a29d05b293867e06ed8b3793cb909b1b435d166deae671a42b88087e2c038"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:00:19.946605Z","signature_b64":"xVqq/Mgzo+s+l3Qpga3Qu/jc0RDxMIEnOfXDpw7YhIqhwVzLnzbBqHpnUbJXdJjP1gc43DrZmeZ50w68Vh20DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4235b9d4e1f7306c4dfcac4a3ded0f02754038e36142bf9a7c521c21f5deff4d","last_reissued_at":"2026-07-05T10:00:19.946185Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:00:19.946185Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bigger Isn't Always Better: Towards a General Prior for Medical Image Reconstruction","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Lukas Glaszner, Martin Zach","submitted_at":"2025-01-13T14:51:15Z","abstract_excerpt":"Diffusion model have been successfully applied to many inverse problems, including MRI and CT reconstruction. Researchers typically re-purpose models originally designed for unconditional sampling without modifications. Using two different posterior sampling algorithms, we show empirically that such large networks are not necessary. Our smallest model, effectively a ResNet, performs almost as good as an attention U-Net on in-distribution reconstruction, while being significantly more robust towards distribution shifts. Furthermore, we introduce models trained on natural images and demonstrate "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.07376","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/2501.07376/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":"2501.07376","created_at":"2026-07-05T10:00:19.946242+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.07376v1","created_at":"2026-07-05T10:00:19.946242+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.07376","created_at":"2026-07-05T10:00:19.946242+00:00"},{"alias_kind":"pith_short_12","alias_value":"II23TVHB64YG","created_at":"2026-07-05T10:00:19.946242+00:00"},{"alias_kind":"pith_short_16","alias_value":"II23TVHB64YGYTP4","created_at":"2026-07-05T10:00:19.946242+00:00"},{"alias_kind":"pith_short_8","alias_value":"II23TVHB","created_at":"2026-07-05T10:00:19.946242+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.20136","citing_title":"PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mapping","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/II23TVHB64YGYTP4VRFD33IPAJ","json":"https://pith.science/pith/II23TVHB64YGYTP4VRFD33IPAJ.json","graph_json":"https://pith.science/api/pith-number/II23TVHB64YGYTP4VRFD33IPAJ/graph.json","events_json":"https://pith.science/api/pith-number/II23TVHB64YGYTP4VRFD33IPAJ/events.json","paper":"https://pith.science/paper/II23TVHB"},"agent_actions":{"view_html":"https://pith.science/pith/II23TVHB64YGYTP4VRFD33IPAJ","download_json":"https://pith.science/pith/II23TVHB64YGYTP4VRFD33IPAJ.json","view_paper":"https://pith.science/paper/II23TVHB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.07376&json=true","fetch_graph":"https://pith.science/api/pith-number/II23TVHB64YGYTP4VRFD33IPAJ/graph.json","fetch_events":"https://pith.science/api/pith-number/II23TVHB64YGYTP4VRFD33IPAJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/II23TVHB64YGYTP4VRFD33IPAJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/II23TVHB64YGYTP4VRFD33IPAJ/action/storage_attestation","attest_author":"https://pith.science/pith/II23TVHB64YGYTP4VRFD33IPAJ/action/author_attestation","sign_citation":"https://pith.science/pith/II23TVHB64YGYTP4VRFD33IPAJ/action/citation_signature","submit_replication":"https://pith.science/pith/II23TVHB64YGYTP4VRFD33IPAJ/action/replication_record"}},"created_at":"2026-07-05T10:00:19.946242+00:00","updated_at":"2026-07-05T10:00:19.946242+00:00"}