{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KMGHDZ5PIKRYSJOM6S46QGV3DQ","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":"9bdf84ffc65f738abe2a5f2e8cc5bd9e65933f1768de3659269ec290db2f5bc4","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2025-05-20T07:18:58Z","title_canon_sha256":"68d5cfe70d3c401756251779fea212b2f8a0cb109b9f773262bb4a7ffb417a8a"},"schema_version":"1.0","source":{"id":"2505.14017","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.14017","created_at":"2026-07-05T11:05:54Z"},{"alias_kind":"arxiv_version","alias_value":"2505.14017v1","created_at":"2026-07-05T11:05:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.14017","created_at":"2026-07-05T11:05:54Z"},{"alias_kind":"pith_short_12","alias_value":"KMGHDZ5PIKRY","created_at":"2026-07-05T11:05:54Z"},{"alias_kind":"pith_short_16","alias_value":"KMGHDZ5PIKRYSJOM","created_at":"2026-07-05T11:05:54Z"},{"alias_kind":"pith_short_8","alias_value":"KMGHDZ5P","created_at":"2026-07-05T11:05:54Z"}],"graph_snapshots":[{"event_id":"sha256:93bdef51138d0f13b7deb3bf641ef5571ce270039958d503986213ad2d68cf6e","target":"graph","created_at":"2026-07-05T11:05:54Z","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/2505.14017/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Surface-based cortical analysis is valuable for a variety of neuroimaging tasks, such as spatial normalization, parcellation, and gray matter (GM) thickness estimation. However, most tools for estimating cortical surfaces work exclusively on scans with at least 1 mm isotropic resolution and are tuned to a specific magnetic resonance (MR) contrast, often T1-weighted (T1w). This precludes application using most clinical MR scans, which are very heterogeneous in terms of contrast and resolution. Here, we use synthetic domain-randomized data to train the first neural network for explicit estimatio","authors_text":"Adrian Dalca, Andrew Hoopes, Axel Thielscher, Colin Magdamo, Jesper Duemose Nielsen, Juan Eugenio Iglesias, Karthik Gopinath, Oula Puonti, Steven Arnold, Sudeshna Das","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2025-05-20T07:18:58Z","title":"End-to-end Cortical Surface Reconstruction from Clinical Magnetic Resonance Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.14017","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:b5fd10470ae528b1630deffd77167d99f82f3cf80fccc114833825f60cc035de","target":"record","created_at":"2026-07-05T11:05:54Z","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":"9bdf84ffc65f738abe2a5f2e8cc5bd9e65933f1768de3659269ec290db2f5bc4","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2025-05-20T07:18:58Z","title_canon_sha256":"68d5cfe70d3c401756251779fea212b2f8a0cb109b9f773262bb4a7ffb417a8a"},"schema_version":"1.0","source":{"id":"2505.14017","kind":"arxiv","version":1}},"canonical_sha256":"530c71e7af42a38925ccf4b9e81abb1c3bde50aa0e48e7aa01cc129a0109a708","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"530c71e7af42a38925ccf4b9e81abb1c3bde50aa0e48e7aa01cc129a0109a708","first_computed_at":"2026-07-05T11:05:54.810328Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:05:54.810328Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"At3NITZ8morCHl/To/mxsmO4TDZ7MYJ+HHjye6qIA40GQSa0e7pVqtTMC1+qaIZVXk8ypa7CP5rp6X3hJtyECA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:05:54.810670Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.14017","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b5fd10470ae528b1630deffd77167d99f82f3cf80fccc114833825f60cc035de","sha256:93bdef51138d0f13b7deb3bf641ef5571ce270039958d503986213ad2d68cf6e"],"state_sha256":"6c5f553ee3bca6f019f2b8dee1ae4ce307ce59a13631bd36274d784f70c4a332"}