{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:PC4762GO5HWMZ3OJ3LA5S4BMMF","short_pith_number":"pith:PC4762GO","canonical_record":{"source":{"id":"2206.03359","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2022-06-07T14:53:35Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"41752f0c210b75bdf3ec9066ca98f8774e2518a459ee294d0f8b87af0997e1a9","abstract_canon_sha256":"d2d7459c0b2e1b51ab2ab667fe838bf3ecda334ed68433de3d1c5874139dc453"},"schema_version":"1.0"},"canonical_sha256":"78b9ff68cee9ecccedc9dac1d9702c6141d8cfd28f81602b5d06c96a2ef0bd2e","source":{"kind":"arxiv","id":"2206.03359","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.03359","created_at":"2026-07-05T07:12:32Z"},{"alias_kind":"arxiv_version","alias_value":"2206.03359v2","created_at":"2026-07-05T07:12:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.03359","created_at":"2026-07-05T07:12:32Z"},{"alias_kind":"pith_short_12","alias_value":"PC4762GO5HWM","created_at":"2026-07-05T07:12:32Z"},{"alias_kind":"pith_short_16","alias_value":"PC4762GO5HWMZ3OJ","created_at":"2026-07-05T07:12:32Z"},{"alias_kind":"pith_short_8","alias_value":"PC4762GO","created_at":"2026-07-05T07:12:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:PC4762GO5HWMZ3OJ3LA5S4BMMF","target":"record","payload":{"canonical_record":{"source":{"id":"2206.03359","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2022-06-07T14:53:35Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"41752f0c210b75bdf3ec9066ca98f8774e2518a459ee294d0f8b87af0997e1a9","abstract_canon_sha256":"d2d7459c0b2e1b51ab2ab667fe838bf3ecda334ed68433de3d1c5874139dc453"},"schema_version":"1.0"},"canonical_sha256":"78b9ff68cee9ecccedc9dac1d9702c6141d8cfd28f81602b5d06c96a2ef0bd2e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:12:32.274992Z","signature_b64":"yQz7Hpqc/Z5cAOupQtO6WpAjy+BbXck2id8VK0SvAfz010o9ZC1Qa6lCmNhNyi422YSPDRScXd8jfxM907bZAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78b9ff68cee9ecccedc9dac1d9702c6141d8cfd28f81602b5d06c96a2ef0bd2e","last_reissued_at":"2026-07-05T07:12:32.274608Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:12:32.274608Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2206.03359","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:12:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QozNIjCjkIufcFcY9nKQ2ppt+5oXpVOqBMrAA9W6+MfrNPsY0eosMRxioMdnVaib3p8oiIFNpkWmLWScQ43MAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T12:15:26.574543Z"},"content_sha256":"c40d47395cc1e885427f1654da1940a7a9a21f8e235c0ce292ac9dd9eb8d87c8","schema_version":"1.0","event_id":"sha256:c40d47395cc1e885427f1654da1940a7a9a21f8e235c0ce292ac9dd9eb8d87c8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:PC4762GO5HWMZ3OJ3LA5S4BMMF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Arman Eshaghi, Daniel C. Alexander, Daniele Ravi (for the Alzheimer's Disease Neuroimaging Initiative), Frederik Barkhof, Geoffrey JM Parker, Lemuel Puglisi","submitted_at":"2022-06-07T14:53:35Z","abstract_excerpt":"Large medical imaging data sets are becoming increasingly available, but ensuring sample quality without significant artefacts is challenging. Existing methods for identifying imperfections in medical imaging rely on data-intensive approaches, compounded by a scarcity of artefact-rich scans for training machine learning models in clinical research. To tackle this problem, we propose a framework with four main components: 1) artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, 2) abstract and engineered features to represent image"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.03359","kind":"arxiv","version":2},"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/2206.03359/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":"2026-07-05T07:12:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q7uKYZy5u6hG3e+u+ujVblmTiptKuLoyuaWrXYNzucaLyDWiRwvUHkQzlM/UxOOZvmn8AAXZ1SA82x6t2La6Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T12:15:26.574891Z"},"content_sha256":"f1d6571ae28ac1b360c3e25833f511c5aa891e8a2ff49c8ecf263652028c32cd","schema_version":"1.0","event_id":"sha256:f1d6571ae28ac1b360c3e25833f511c5aa891e8a2ff49c8ecf263652028c32cd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PC4762GO5HWMZ3OJ3LA5S4BMMF/bundle.json","state_url":"https://pith.science/pith/PC4762GO5HWMZ3OJ3LA5S4BMMF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PC4762GO5HWMZ3OJ3LA5S4BMMF/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":"2026-08-19T12:15:26Z","links":{"resolver":"https://pith.science/pith/PC4762GO5HWMZ3OJ3LA5S4BMMF","bundle":"https://pith.science/pith/PC4762GO5HWMZ3OJ3LA5S4BMMF/bundle.json","state":"https://pith.science/pith/PC4762GO5HWMZ3OJ3LA5S4BMMF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PC4762GO5HWMZ3OJ3LA5S4BMMF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:PC4762GO5HWMZ3OJ3LA5S4BMMF","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":"d2d7459c0b2e1b51ab2ab667fe838bf3ecda334ed68433de3d1c5874139dc453","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2022-06-07T14:53:35Z","title_canon_sha256":"41752f0c210b75bdf3ec9066ca98f8774e2518a459ee294d0f8b87af0997e1a9"},"schema_version":"1.0","source":{"id":"2206.03359","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.03359","created_at":"2026-07-05T07:12:32Z"},{"alias_kind":"arxiv_version","alias_value":"2206.03359v2","created_at":"2026-07-05T07:12:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.03359","created_at":"2026-07-05T07:12:32Z"},{"alias_kind":"pith_short_12","alias_value":"PC4762GO5HWM","created_at":"2026-07-05T07:12:32Z"},{"alias_kind":"pith_short_16","alias_value":"PC4762GO5HWMZ3OJ","created_at":"2026-07-05T07:12:32Z"},{"alias_kind":"pith_short_8","alias_value":"PC4762GO","created_at":"2026-07-05T07:12:32Z"}],"graph_snapshots":[{"event_id":"sha256:f1d6571ae28ac1b360c3e25833f511c5aa891e8a2ff49c8ecf263652028c32cd","target":"graph","created_at":"2026-07-05T07:12:32Z","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/2206.03359/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large medical imaging data sets are becoming increasingly available, but ensuring sample quality without significant artefacts is challenging. Existing methods for identifying imperfections in medical imaging rely on data-intensive approaches, compounded by a scarcity of artefact-rich scans for training machine learning models in clinical research. To tackle this problem, we propose a framework with four main components: 1) artefact generators inspired by magnetic resonance physics to corrupt brain MRI scans and augment a training dataset, 2) abstract and engineered features to represent image","authors_text":"Arman Eshaghi, Daniel C. Alexander, Daniele Ravi (for the Alzheimer's Disease Neuroimaging Initiative), Frederik Barkhof, Geoffrey JM Parker, Lemuel Puglisi","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2022-06-07T14:53:35Z","title":"An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.03359","kind":"arxiv","version":2},"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:c40d47395cc1e885427f1654da1940a7a9a21f8e235c0ce292ac9dd9eb8d87c8","target":"record","created_at":"2026-07-05T07:12:32Z","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":"d2d7459c0b2e1b51ab2ab667fe838bf3ecda334ed68433de3d1c5874139dc453","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2022-06-07T14:53:35Z","title_canon_sha256":"41752f0c210b75bdf3ec9066ca98f8774e2518a459ee294d0f8b87af0997e1a9"},"schema_version":"1.0","source":{"id":"2206.03359","kind":"arxiv","version":2}},"canonical_sha256":"78b9ff68cee9ecccedc9dac1d9702c6141d8cfd28f81602b5d06c96a2ef0bd2e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"78b9ff68cee9ecccedc9dac1d9702c6141d8cfd28f81602b5d06c96a2ef0bd2e","first_computed_at":"2026-07-05T07:12:32.274608Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:12:32.274608Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yQz7Hpqc/Z5cAOupQtO6WpAjy+BbXck2id8VK0SvAfz010o9ZC1Qa6lCmNhNyi422YSPDRScXd8jfxM907bZAg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:12:32.274992Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.03359","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c40d47395cc1e885427f1654da1940a7a9a21f8e235c0ce292ac9dd9eb8d87c8","sha256:f1d6571ae28ac1b360c3e25833f511c5aa891e8a2ff49c8ecf263652028c32cd"],"state_sha256":"c6e2050d964f5dd66ac48304d015db19fa0a8b7f2ed683333aa5ef28906fb010"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JsQ/iSCcUf+8TfVAh8doYVkF2MU31luUeZ/DCIpIW+o6ko7sFio43Yk+bojcrV1l9cW4CUiDK+LeVTmV4hUHAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T12:15:26.578567Z","bundle_sha256":"f7ceac355ba6c912811017674f7000817c742808c74c26a42cb155d5c3e450cc"}}