{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:IQEQ7AJNEPXD2NJ57C65O3A3S7","short_pith_number":"pith:IQEQ7AJN","canonical_record":{"source":{"id":"2606.18489","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-06-16T21:04:03Z","cross_cats_sorted":[],"title_canon_sha256":"335c55e0ce9239cf72879c75d6c7a660e0656db6b6cd1efb71711f0633bdcfe6","abstract_canon_sha256":"3e7fbe0dab7c292d16b6b4518c7d20c334a793fdedf04fe17bf1aaa4c7e499cc"},"schema_version":"1.0"},"canonical_sha256":"44090f812d23ee3d353df8bdd76c1b97e2ea1ef12ee5d9aa8dd60cf907d4b5a3","source":{"kind":"arxiv","id":"2606.18489","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.18489","created_at":"2026-06-19T16:11:02Z"},{"alias_kind":"arxiv_version","alias_value":"2606.18489v1","created_at":"2026-06-19T16:11:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.18489","created_at":"2026-06-19T16:11:02Z"},{"alias_kind":"pith_short_12","alias_value":"IQEQ7AJNEPXD","created_at":"2026-06-19T16:11:02Z"},{"alias_kind":"pith_short_16","alias_value":"IQEQ7AJNEPXD2NJ5","created_at":"2026-06-19T16:11:02Z"},{"alias_kind":"pith_short_8","alias_value":"IQEQ7AJN","created_at":"2026-06-19T16:11:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:IQEQ7AJNEPXD2NJ57C65O3A3S7","target":"record","payload":{"canonical_record":{"source":{"id":"2606.18489","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-06-16T21:04:03Z","cross_cats_sorted":[],"title_canon_sha256":"335c55e0ce9239cf72879c75d6c7a660e0656db6b6cd1efb71711f0633bdcfe6","abstract_canon_sha256":"3e7fbe0dab7c292d16b6b4518c7d20c334a793fdedf04fe17bf1aaa4c7e499cc"},"schema_version":"1.0"},"canonical_sha256":"44090f812d23ee3d353df8bdd76c1b97e2ea1ef12ee5d9aa8dd60cf907d4b5a3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:11:02.332859Z","signature_b64":"UQ3pRFLXmFHEkbOkhHaB+iDwgXLr8FbY+Z/DUtZrU3dxNm+v2Vb99qu2gQlzAtTAMwxx5UO/rXi5DvQO3aZFAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"44090f812d23ee3d353df8bdd76c1b97e2ea1ef12ee5d9aa8dd60cf907d4b5a3","last_reissued_at":"2026-06-19T16:11:02.332518Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:11:02.332518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.18489","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":"2026-06-19T16:11:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E0zm1NtXUx6iaMVNT11f29qFR40PcReydP0GRL4dYO7dtqmNuy6+L3QwFYSL8GjSuiulMmSdpSASzuad92QJDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T17:34:41.131051Z"},"content_sha256":"ed5e5d044d930bbcac11e576f49c462c857fce86d1fe7f10e6447fd655869646","schema_version":"1.0","event_id":"sha256:ed5e5d044d930bbcac11e576f49c462c857fce86d1fe7f10e6447fd655869646"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:IQEQ7AJNEPXD2NJ57C65O3A3S7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GHOST-CAT: An Efficient and Practical Network for Mesh Generation from 3D Echocardiography","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Alistair A. Young, Debbie Zhao, Edward Ferdian, Martyn P. Nash","submitted_at":"2026-06-16T21:04:03Z","abstract_excerpt":"Recent advances in deep learning have significantly accelerated cardiac imaging workflows, from segmentation to the generation of meshes for computational modelling. Nevertheless, analysis of 3D echocardiograms presents unique challenges due to their low contrast-to-noise ratio, conical field of view, and susceptibility to acoustic shadowing. Here, we present an efficient and practical network tailored for 3D echocardiograms. Our method consists of a two-stage network that combines convolutional neural networks, graph convolutional networks, and transformers, to create accurate time-varying 3D"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.18489","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/2606.18489/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":"2026-06-19T16:11:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JFTVdKVLY4CyRI2u6Sbz7vOHIm5mwlZjcpTg4sT6bsK9CPdFa85NHEYBCv6vfWtuys2tcypsh319xSTS/I64Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T17:34:41.131440Z"},"content_sha256":"d464d652b6b01b6d1e55425f2a362c83151d2154706b07369caab1c9db3d5ea5","schema_version":"1.0","event_id":"sha256:d464d652b6b01b6d1e55425f2a362c83151d2154706b07369caab1c9db3d5ea5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IQEQ7AJNEPXD2NJ57C65O3A3S7/bundle.json","state_url":"https://pith.science/pith/IQEQ7AJNEPXD2NJ57C65O3A3S7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IQEQ7AJNEPXD2NJ57C65O3A3S7/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":"2026-06-25T17:34:41Z","links":{"resolver":"https://pith.science/pith/IQEQ7AJNEPXD2NJ57C65O3A3S7","bundle":"https://pith.science/pith/IQEQ7AJNEPXD2NJ57C65O3A3S7/bundle.json","state":"https://pith.science/pith/IQEQ7AJNEPXD2NJ57C65O3A3S7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IQEQ7AJNEPXD2NJ57C65O3A3S7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:IQEQ7AJNEPXD2NJ57C65O3A3S7","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":"3e7fbe0dab7c292d16b6b4518c7d20c334a793fdedf04fe17bf1aaa4c7e499cc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-06-16T21:04:03Z","title_canon_sha256":"335c55e0ce9239cf72879c75d6c7a660e0656db6b6cd1efb71711f0633bdcfe6"},"schema_version":"1.0","source":{"id":"2606.18489","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.18489","created_at":"2026-06-19T16:11:02Z"},{"alias_kind":"arxiv_version","alias_value":"2606.18489v1","created_at":"2026-06-19T16:11:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.18489","created_at":"2026-06-19T16:11:02Z"},{"alias_kind":"pith_short_12","alias_value":"IQEQ7AJNEPXD","created_at":"2026-06-19T16:11:02Z"},{"alias_kind":"pith_short_16","alias_value":"IQEQ7AJNEPXD2NJ5","created_at":"2026-06-19T16:11:02Z"},{"alias_kind":"pith_short_8","alias_value":"IQEQ7AJN","created_at":"2026-06-19T16:11:02Z"}],"graph_snapshots":[{"event_id":"sha256:d464d652b6b01b6d1e55425f2a362c83151d2154706b07369caab1c9db3d5ea5","target":"graph","created_at":"2026-06-19T16:11:02Z","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/2606.18489/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in deep learning have significantly accelerated cardiac imaging workflows, from segmentation to the generation of meshes for computational modelling. Nevertheless, analysis of 3D echocardiograms presents unique challenges due to their low contrast-to-noise ratio, conical field of view, and susceptibility to acoustic shadowing. Here, we present an efficient and practical network tailored for 3D echocardiograms. Our method consists of a two-stage network that combines convolutional neural networks, graph convolutional networks, and transformers, to create accurate time-varying 3D","authors_text":"Alistair A. Young, Debbie Zhao, Edward Ferdian, Martyn P. Nash","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-06-16T21:04:03Z","title":"GHOST-CAT: An Efficient and Practical Network for Mesh Generation from 3D Echocardiography"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.18489","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:ed5e5d044d930bbcac11e576f49c462c857fce86d1fe7f10e6447fd655869646","target":"record","created_at":"2026-06-19T16:11:02Z","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":"3e7fbe0dab7c292d16b6b4518c7d20c334a793fdedf04fe17bf1aaa4c7e499cc","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-06-16T21:04:03Z","title_canon_sha256":"335c55e0ce9239cf72879c75d6c7a660e0656db6b6cd1efb71711f0633bdcfe6"},"schema_version":"1.0","source":{"id":"2606.18489","kind":"arxiv","version":1}},"canonical_sha256":"44090f812d23ee3d353df8bdd76c1b97e2ea1ef12ee5d9aa8dd60cf907d4b5a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"44090f812d23ee3d353df8bdd76c1b97e2ea1ef12ee5d9aa8dd60cf907d4b5a3","first_computed_at":"2026-06-19T16:11:02.332518Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-19T16:11:02.332518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UQ3pRFLXmFHEkbOkhHaB+iDwgXLr8FbY+Z/DUtZrU3dxNm+v2Vb99qu2gQlzAtTAMwxx5UO/rXi5DvQO3aZFAw==","signature_status":"signed_v1","signed_at":"2026-06-19T16:11:02.332859Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.18489","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ed5e5d044d930bbcac11e576f49c462c857fce86d1fe7f10e6447fd655869646","sha256:d464d652b6b01b6d1e55425f2a362c83151d2154706b07369caab1c9db3d5ea5"],"state_sha256":"802066c06b42ec72e0a04b465c8dd801857a1e9a215c006d299d6cfa4b64ae14"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wc292gxLwk+vDYnJxlToJGoUKBp/5yD4y5kAmlyyBRGrSdqkL3t1PtSWVxIFcLQkxftmNKisTJWiAcC5BmEcBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-25T17:34:41.133378Z","bundle_sha256":"797f6c82889d96f3122588b191b4ddd383ed8eb0ddb6f4fc0f75b3fbd803e29c"}}