{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:5LYXXOS45YODD6M4QPJDRCHQJW","short_pith_number":"pith:5LYXXOS4","canonical_record":{"source":{"id":"2409.08463","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2024-09-13T01:23:28Z","cross_cats_sorted":[],"title_canon_sha256":"00706be786c1551a557c9577a6140bd1d26d0307bff0b85016a34d4132354ef8","abstract_canon_sha256":"47ec3646a2fe8fcfec4d85eef8289e9f6904381ab021a11a6e539984e6ce2637"},"schema_version":"1.0"},"canonical_sha256":"eaf17bba5cee1c31f99c83d23888f04dae057f3ffc42bfaf15807de1b2c236b6","source":{"kind":"arxiv","id":"2409.08463","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.08463","created_at":"2026-07-05T09:06:31Z"},{"alias_kind":"arxiv_version","alias_value":"2409.08463v1","created_at":"2026-07-05T09:06:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.08463","created_at":"2026-07-05T09:06:31Z"},{"alias_kind":"pith_short_12","alias_value":"5LYXXOS45YOD","created_at":"2026-07-05T09:06:31Z"},{"alias_kind":"pith_short_16","alias_value":"5LYXXOS45YODD6M4","created_at":"2026-07-05T09:06:31Z"},{"alias_kind":"pith_short_8","alias_value":"5LYXXOS4","created_at":"2026-07-05T09:06:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:5LYXXOS45YODD6M4QPJDRCHQJW","target":"record","payload":{"canonical_record":{"source":{"id":"2409.08463","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2024-09-13T01:23:28Z","cross_cats_sorted":[],"title_canon_sha256":"00706be786c1551a557c9577a6140bd1d26d0307bff0b85016a34d4132354ef8","abstract_canon_sha256":"47ec3646a2fe8fcfec4d85eef8289e9f6904381ab021a11a6e539984e6ce2637"},"schema_version":"1.0"},"canonical_sha256":"eaf17bba5cee1c31f99c83d23888f04dae057f3ffc42bfaf15807de1b2c236b6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:06:31.771471Z","signature_b64":"1dBi3ilzzy9kNe6dpqbkvlQbVkEUzuzxVQA31nhtxkKvuA57cXTdimDr2Al4wzIpzA/ynzXxtsybi2ZjNDfrAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eaf17bba5cee1c31f99c83d23888f04dae057f3ffc42bfaf15807de1b2c236b6","last_reissued_at":"2026-07-05T09:06:31.771123Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:06:31.771123Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2409.08463","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:06:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+aSl+cUY6Jy7nMXDwWn7WbAR7eOtsn1BTMb8WZOdBfRv85GZfy20Za7a5bhBTuWJ3VTyhYdu2ltF/eKhrajcDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T06:13:16.880900Z"},"content_sha256":"122797a68ee39799c5df2bb3243e13f8c4ca2da7a26faacc1a5a44b323537ac7","schema_version":"1.0","event_id":"sha256:122797a68ee39799c5df2bb3243e13f8c4ca2da7a26faacc1a5a44b323537ac7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:5LYXXOS45YODD6M4QPJDRCHQJW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Evaluating the Quality of Brain MRI Generators","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Binxu Li, Jiaqi Wu, Kilian M. Pohl, Wei Peng, Yu Zhang","submitted_at":"2024-09-13T01:23:28Z","abstract_excerpt":"Deep learning models generating structural brain MRIs have the potential to significantly accelerate discovery of neuroscience studies. However, their use has been limited in part by the way their quality is evaluated. Most evaluations of generative models focus on metrics originally designed for natural images (such as structural similarity index and Frechet inception distance). As we show in a comparison of 6 state-of-the-art generative models trained and tested on over 3000 MRIs, these metrics are sensitive to the experimental setup and inadequately assess how well brain MRIs capture macros"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.08463","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/2409.08463/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:06:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+PsjkByMrUWsiWJmBrqy13v6yQsUTiCF0hhDtASUbAfTntd8LdMNBgZek6yhY3149v04jb/w9UwgMIdUsojUDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T06:13:16.881279Z"},"content_sha256":"ae7bf51c8f1cc3c35e6a6788f0bf64ce0f05540e13ebfcb93b65b97c72084ed7","schema_version":"1.0","event_id":"sha256:ae7bf51c8f1cc3c35e6a6788f0bf64ce0f05540e13ebfcb93b65b97c72084ed7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5LYXXOS45YODD6M4QPJDRCHQJW/bundle.json","state_url":"https://pith.science/pith/5LYXXOS45YODD6M4QPJDRCHQJW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5LYXXOS45YODD6M4QPJDRCHQJW/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-06T06:13:16Z","links":{"resolver":"https://pith.science/pith/5LYXXOS45YODD6M4QPJDRCHQJW","bundle":"https://pith.science/pith/5LYXXOS45YODD6M4QPJDRCHQJW/bundle.json","state":"https://pith.science/pith/5LYXXOS45YODD6M4QPJDRCHQJW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5LYXXOS45YODD6M4QPJDRCHQJW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:5LYXXOS45YODD6M4QPJDRCHQJW","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"47ec3646a2fe8fcfec4d85eef8289e9f6904381ab021a11a6e539984e6ce2637","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2024-09-13T01:23:28Z","title_canon_sha256":"00706be786c1551a557c9577a6140bd1d26d0307bff0b85016a34d4132354ef8"},"schema_version":"1.0","source":{"id":"2409.08463","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.08463","created_at":"2026-07-05T09:06:31Z"},{"alias_kind":"arxiv_version","alias_value":"2409.08463v1","created_at":"2026-07-05T09:06:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.08463","created_at":"2026-07-05T09:06:31Z"},{"alias_kind":"pith_short_12","alias_value":"5LYXXOS45YOD","created_at":"2026-07-05T09:06:31Z"},{"alias_kind":"pith_short_16","alias_value":"5LYXXOS45YODD6M4","created_at":"2026-07-05T09:06:31Z"},{"alias_kind":"pith_short_8","alias_value":"5LYXXOS4","created_at":"2026-07-05T09:06:31Z"}],"graph_snapshots":[{"event_id":"sha256:ae7bf51c8f1cc3c35e6a6788f0bf64ce0f05540e13ebfcb93b65b97c72084ed7","target":"graph","created_at":"2026-07-05T09:06:31Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2409.08463/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning models generating structural brain MRIs have the potential to significantly accelerate discovery of neuroscience studies. However, their use has been limited in part by the way their quality is evaluated. Most evaluations of generative models focus on metrics originally designed for natural images (such as structural similarity index and Frechet inception distance). As we show in a comparison of 6 state-of-the-art generative models trained and tested on over 3000 MRIs, these metrics are sensitive to the experimental setup and inadequately assess how well brain MRIs capture macros","authors_text":"Binxu Li, Jiaqi Wu, Kilian M. Pohl, Wei Peng, Yu Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2024-09-13T01:23:28Z","title":"Evaluating the Quality of Brain MRI Generators"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.08463","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:122797a68ee39799c5df2bb3243e13f8c4ca2da7a26faacc1a5a44b323537ac7","target":"record","created_at":"2026-07-05T09:06:31Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"47ec3646a2fe8fcfec4d85eef8289e9f6904381ab021a11a6e539984e6ce2637","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2024-09-13T01:23:28Z","title_canon_sha256":"00706be786c1551a557c9577a6140bd1d26d0307bff0b85016a34d4132354ef8"},"schema_version":"1.0","source":{"id":"2409.08463","kind":"arxiv","version":1}},"canonical_sha256":"eaf17bba5cee1c31f99c83d23888f04dae057f3ffc42bfaf15807de1b2c236b6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"eaf17bba5cee1c31f99c83d23888f04dae057f3ffc42bfaf15807de1b2c236b6","first_computed_at":"2026-07-05T09:06:31.771123Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:06:31.771123Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1dBi3ilzzy9kNe6dpqbkvlQbVkEUzuzxVQA31nhtxkKvuA57cXTdimDr2Al4wzIpzA/ynzXxtsybi2ZjNDfrAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:06:31.771471Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.08463","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:122797a68ee39799c5df2bb3243e13f8c4ca2da7a26faacc1a5a44b323537ac7","sha256:ae7bf51c8f1cc3c35e6a6788f0bf64ce0f05540e13ebfcb93b65b97c72084ed7"],"state_sha256":"d428208e604bf8a2a95aea1a66d4a0675c67bc6a571ae6882fd440cc64ed4051"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RwSaz73YPvWCd3oQ3LWfmWCPl+MZgmXfiOLN5/bmDoRpAonHK/TJvadOEYlBrm7Qurj5QycvoFXHomoclDUCBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T06:13:16.883465Z","bundle_sha256":"c5c08e2d7c1c8bbd9bb00eb951fb81336c7aa19b4dc80f7a76fb0a5f4524f08f"}}