{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TVR7D5ZA5CJH77EM6PI4VFJTBZ","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":"c08a2d5aa2cf297afc813ee1a3d906b18bcbf8c8a6e7c6c8e890a13fa229ca16","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-07-29T09:24:54Z","title_canon_sha256":"369c3a193ead15514e621aa1ac584f46e2997b59b1757994b3bb56efbe3b03c3"},"schema_version":"1.0","source":{"id":"1807.10831","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.10831","created_at":"2026-07-05T01:40:55Z"},{"alias_kind":"arxiv_version","alias_value":"1807.10831v1","created_at":"2026-07-05T01:40:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.10831","created_at":"2026-07-05T01:40:55Z"},{"alias_kind":"pith_short_12","alias_value":"TVR7D5ZA5CJH","created_at":"2026-07-05T01:40:55Z"},{"alias_kind":"pith_short_16","alias_value":"TVR7D5ZA5CJH77EM","created_at":"2026-07-05T01:40:55Z"},{"alias_kind":"pith_short_8","alias_value":"TVR7D5ZA","created_at":"2026-07-05T01:40:55Z"}],"graph_snapshots":[{"event_id":"sha256:0b41e547ce01f131d8c06f868f2bbd5be5a0a129060d181dcac777790bf7d2ba","target":"graph","created_at":"2026-07-05T01:40:55Z","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/1807.10831/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Purpose: The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper is to develop a standalone novel technique to suppress motion artefacts from MR images using a data-driven deep learning approach. Methods: A deep learning convolutional neural network (CNN) was developed to remove motion artefacts in brain MR images. A CNN was trained on simulated motion corrupted images to identify and suppress artefacts due to the motion. The network was an encoder-decoder CNN architecture where the encoder decomposed the motion corrupted images into a set of feature","authors_text":"Gary F. Egan, Kamlesh Pawar, N. Jon Shah, Zhaolin Chen","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-07-29T09:24:54Z","title":"MoCoNet: Motion Correction in 3D MPRAGE images using a Convolutional Neural Network approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.10831","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":"sha256:6ff77c44705fd28461a0aacd39e7bc438533617ae48b77bacb45668fa3300dd7","target":"record","created_at":"2026-07-05T01:40:55Z","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":"c08a2d5aa2cf297afc813ee1a3d906b18bcbf8c8a6e7c6c8e890a13fa229ca16","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-07-29T09:24:54Z","title_canon_sha256":"369c3a193ead15514e621aa1ac584f46e2997b59b1757994b3bb56efbe3b03c3"},"schema_version":"1.0","source":{"id":"1807.10831","kind":"arxiv","version":1}},"canonical_sha256":"9d63f1f720e8927ffc8cf3d1ca95330e672626da115c1b9a32183f17e808fb98","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9d63f1f720e8927ffc8cf3d1ca95330e672626da115c1b9a32183f17e808fb98","first_computed_at":"2026-07-05T01:40:55.159447Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:40:55.159447Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"t3AZCldjHjqCPYW0vbzhzMqIkdaGRsyZVE+ERPYxlU/6FWExhI2K8HDg+vj3mq7Cebjmjcmt4k9bhURoXvz0Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:40:55.159910Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.10831","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6ff77c44705fd28461a0aacd39e7bc438533617ae48b77bacb45668fa3300dd7","sha256:0b41e547ce01f131d8c06f868f2bbd5be5a0a129060d181dcac777790bf7d2ba"],"state_sha256":"60bb51542172c5fdeec605b5b8af4c1c757c9277d0c2515af38aa829d1fc7d4b"}