{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:B2PM6BRVDL4NIBTMQO5UD4CRKZ","short_pith_number":"pith:B2PM6BRV","canonical_record":{"source":{"id":"1903.04961","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2019-03-08T21:49:37Z","cross_cats_sorted":[],"title_canon_sha256":"9f2c18d24b88730c396209c9d0804efb2a38fa39419453eed7157734d6a4c9d6","abstract_canon_sha256":"306906250e53150af6286e67d7fdcfb5e91f8b7613cee4edb33966eea2c28423"},"schema_version":"1.0"},"canonical_sha256":"0e9ecf06351af8d4066c83bb41f05156547fb4d4605b9b13aaa569a3756e99b1","source":{"kind":"arxiv","id":"1903.04961","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.04961","created_at":"2026-05-17T23:51:25Z"},{"alias_kind":"arxiv_version","alias_value":"1903.04961v2","created_at":"2026-05-17T23:51:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.04961","created_at":"2026-05-17T23:51:25Z"},{"alias_kind":"pith_short_12","alias_value":"B2PM6BRVDL4N","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"B2PM6BRVDL4NIBTM","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"B2PM6BRV","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:B2PM6BRVDL4NIBTMQO5UD4CRKZ","target":"record","payload":{"canonical_record":{"source":{"id":"1903.04961","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2019-03-08T21:49:37Z","cross_cats_sorted":[],"title_canon_sha256":"9f2c18d24b88730c396209c9d0804efb2a38fa39419453eed7157734d6a4c9d6","abstract_canon_sha256":"306906250e53150af6286e67d7fdcfb5e91f8b7613cee4edb33966eea2c28423"},"schema_version":"1.0"},"canonical_sha256":"0e9ecf06351af8d4066c83bb41f05156547fb4d4605b9b13aaa569a3756e99b1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:25.478659Z","signature_b64":"7+QrG47VR5lIpxF9v/mQ6uP8vDwY71nF7DRZlfDEXlHajOCcCcltDgnoV7QfXa8n0N3XsFIfRomXVaVvqxnbCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e9ecf06351af8d4066c83bb41f05156547fb4d4605b9b13aaa569a3756e99b1","last_reissued_at":"2026-05-17T23:51:25.478088Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:25.478088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.04961","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":"2026-05-17T23:51:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9ip2jdGspzc97rhFuUnBzgoxZsK/gJc9el80EUDB1dMg6oi4wxkd9peCd/LRVXuGNjuhEKBsfGqNE2lGFBWEDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T17:57:12.891923Z"},"content_sha256":"c4f51b03ad27a107867e2cd0836942dfd1bbd29c69b8a3311c59714a8f55bc82","schema_version":"1.0","event_id":"sha256:c4f51b03ad27a107867e2cd0836942dfd1bbd29c69b8a3311c59714a8f55bc82"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:B2PM6BRVDL4NIBTMQO5UD4CRKZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Quantitative Susceptibility Inversion Through Parcellated Multiresolution Neural Networks and K-Space Substitution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.med-ph","authors_text":"Andrew S. Nencka, Brad Swearingen, Juan Liu, Kevin M. Koch, L. Tugan Muftuler, Robin Karr","submitted_at":"2019-03-08T21:49:37Z","abstract_excerpt":"Purpose: Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by poor conditioning of the field to susceptibility transformation. State-of-art QSM reconstruction methods either suffer from image artifacts or long computation times, which limits QSM clinical translation efforts. To overcome these limitations, a deep-learning-based approach is proposed and demonstrated. Methods: An encoder-decoder neural network was trained to infer susceptibility maps on volume parcellated regions. The training data consisted of fabricated susceptibility distributions"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.04961","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":""},"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":"2026-05-17T23:51:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hJnNsEC3a3Cv8zYS2PVs7FeJlrV2DVK2XarpFkTcd2PY5tGMNz2XSLO8ReL9L+3qlt4A4nS8Va+mu5Ffj8BLDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T17:57:12.892270Z"},"content_sha256":"0263c67e4272dcd97b1fa4d05ae885add5aa67ad2dbb6bd69ee0f01baaf25a82","schema_version":"1.0","event_id":"sha256:0263c67e4272dcd97b1fa4d05ae885add5aa67ad2dbb6bd69ee0f01baaf25a82"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B2PM6BRVDL4NIBTMQO5UD4CRKZ/bundle.json","state_url":"https://pith.science/pith/B2PM6BRVDL4NIBTMQO5UD4CRKZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B2PM6BRVDL4NIBTMQO5UD4CRKZ/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":"2026-06-01T17:57:12Z","links":{"resolver":"https://pith.science/pith/B2PM6BRVDL4NIBTMQO5UD4CRKZ","bundle":"https://pith.science/pith/B2PM6BRVDL4NIBTMQO5UD4CRKZ/bundle.json","state":"https://pith.science/pith/B2PM6BRVDL4NIBTMQO5UD4CRKZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B2PM6BRVDL4NIBTMQO5UD4CRKZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:B2PM6BRVDL4NIBTMQO5UD4CRKZ","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":"306906250e53150af6286e67d7fdcfb5e91f8b7613cee4edb33966eea2c28423","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2019-03-08T21:49:37Z","title_canon_sha256":"9f2c18d24b88730c396209c9d0804efb2a38fa39419453eed7157734d6a4c9d6"},"schema_version":"1.0","source":{"id":"1903.04961","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.04961","created_at":"2026-05-17T23:51:25Z"},{"alias_kind":"arxiv_version","alias_value":"1903.04961v2","created_at":"2026-05-17T23:51:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.04961","created_at":"2026-05-17T23:51:25Z"},{"alias_kind":"pith_short_12","alias_value":"B2PM6BRVDL4N","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"B2PM6BRVDL4NIBTM","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"B2PM6BRV","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:0263c67e4272dcd97b1fa4d05ae885add5aa67ad2dbb6bd69ee0f01baaf25a82","target":"graph","created_at":"2026-05-17T23:51:25Z","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"},"paper":{"abstract_excerpt":"Purpose: Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by poor conditioning of the field to susceptibility transformation. State-of-art QSM reconstruction methods either suffer from image artifacts or long computation times, which limits QSM clinical translation efforts. To overcome these limitations, a deep-learning-based approach is proposed and demonstrated. Methods: An encoder-decoder neural network was trained to infer susceptibility maps on volume parcellated regions. The training data consisted of fabricated susceptibility distributions","authors_text":"Andrew S. Nencka, Brad Swearingen, Juan Liu, Kevin M. Koch, L. Tugan Muftuler, Robin Karr","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2019-03-08T21:49:37Z","title":"Quantitative Susceptibility Inversion Through Parcellated Multiresolution Neural Networks and K-Space Substitution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.04961","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:c4f51b03ad27a107867e2cd0836942dfd1bbd29c69b8a3311c59714a8f55bc82","target":"record","created_at":"2026-05-17T23:51:25Z","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":"306906250e53150af6286e67d7fdcfb5e91f8b7613cee4edb33966eea2c28423","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2019-03-08T21:49:37Z","title_canon_sha256":"9f2c18d24b88730c396209c9d0804efb2a38fa39419453eed7157734d6a4c9d6"},"schema_version":"1.0","source":{"id":"1903.04961","kind":"arxiv","version":2}},"canonical_sha256":"0e9ecf06351af8d4066c83bb41f05156547fb4d4605b9b13aaa569a3756e99b1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0e9ecf06351af8d4066c83bb41f05156547fb4d4605b9b13aaa569a3756e99b1","first_computed_at":"2026-05-17T23:51:25.478088Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:25.478088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7+QrG47VR5lIpxF9v/mQ6uP8vDwY71nF7DRZlfDEXlHajOCcCcltDgnoV7QfXa8n0N3XsFIfRomXVaVvqxnbCA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:25.478659Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.04961","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c4f51b03ad27a107867e2cd0836942dfd1bbd29c69b8a3311c59714a8f55bc82","sha256:0263c67e4272dcd97b1fa4d05ae885add5aa67ad2dbb6bd69ee0f01baaf25a82"],"state_sha256":"562183f9008dd02eee8901422ff18f1b0dd469aae5288842aef9b86127981702"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S27fpqB5I6jEwdfowmXamb2w2+t/FpkbLKcybep8EPXY2aw0H+/HBUhVBA333MVdiX1T+i8f7yR6CFHhGnUJBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T17:57:12.894330Z","bundle_sha256":"eb50d60db6f0914f80a42c2935249597a642c89d6a652870ef3dacd1f11d5cae"}}