{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:MYBHC33FG7E2QJFIUIEIRWJWH5","short_pith_number":"pith:MYBHC33F","canonical_record":{"source":{"id":"1807.02152","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-07-05T18:56:14Z","cross_cats_sorted":[],"title_canon_sha256":"15697fc6c1ffb9f55d0d8ed533ad61a0645065c1a03ee5f0ec8eb99f5670a9e4","abstract_canon_sha256":"1c42d1ca559a9d151f2d25c56572cff58ed6bbc1bd2474c15fb50c5532a54815"},"schema_version":"1.0"},"canonical_sha256":"6602716f6537c9a824a8a20888d9363f7ad56100ec63f096177991369730a85d","source":{"kind":"arxiv","id":"1807.02152","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.02152","created_at":"2026-05-18T00:11:22Z"},{"alias_kind":"arxiv_version","alias_value":"1807.02152v1","created_at":"2026-05-18T00:11:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02152","created_at":"2026-05-18T00:11:22Z"},{"alias_kind":"pith_short_12","alias_value":"MYBHC33FG7E2","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"MYBHC33FG7E2QJFI","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"MYBHC33F","created_at":"2026-05-18T12:32:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:MYBHC33FG7E2QJFIUIEIRWJWH5","target":"record","payload":{"canonical_record":{"source":{"id":"1807.02152","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-07-05T18:56:14Z","cross_cats_sorted":[],"title_canon_sha256":"15697fc6c1ffb9f55d0d8ed533ad61a0645065c1a03ee5f0ec8eb99f5670a9e4","abstract_canon_sha256":"1c42d1ca559a9d151f2d25c56572cff58ed6bbc1bd2474c15fb50c5532a54815"},"schema_version":"1.0"},"canonical_sha256":"6602716f6537c9a824a8a20888d9363f7ad56100ec63f096177991369730a85d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:22.965412Z","signature_b64":"e/kJS9qeLzGthnvm2G1aK2LxzGkPIy4tfJKeKQptCPdpu+AahDdIzfJuEe76wQHs6ctTXQmCmWwbFYxoAUgqDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6602716f6537c9a824a8a20888d9363f7ad56100ec63f096177991369730a85d","last_reissued_at":"2026-05-18T00:11:22.964844Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:22.964844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.02152","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":"2026-05-18T00:11:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"twCckmQnfdiVxko0nhTR5uu86076J08yfZdIo3MuuZzTvrcKiK3t4SxLBhLksXHn9jzaFwVigxhBggUiwPZFAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T05:26:35.591144Z"},"content_sha256":"005e360487f176d4c3f42e8ad4301c02d233cbf9c6a36cfaa2eaad8cdb0ed770","schema_version":"1.0","event_id":"sha256:005e360487f176d4c3f42e8ad4301c02d233cbf9c6a36cfaa2eaad8cdb0ed770"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:MYBHC33FG7E2QJFIUIEIRWJWH5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ashirbani Saha, Brian J. Soher, Jun Zhang, Maciej A. Mazurowski","submitted_at":"2018-07-05T18:56:14Z","abstract_excerpt":"Objective: To develop an automatic image normalization algorithm for intensity correction of images from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired by different MRI scanners with various imaging parameters, using only image information. Methods: DCE-MR images of 460 subjects with breast cancer acquired by different scanners were used in this study. Each subject had one T1-weighted pre-contrast image and three T1-weighted post-contrast images available. Our normalization algorithm operated under the assumption that the same type of tissue in different patient"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02152","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":""},"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":"2026-05-18T00:11:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vZjcrUWFuQfl7EEFdDGqAC75FmmeijoJY9xtzky8iHHeQWR2NGsk5lSIRltb6G6bKUcSbt3/pE1cKWIywgCVBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T05:26:35.591515Z"},"content_sha256":"b835c6c78ed3e8bb0f0a55a6bb8d2a25d58de7469b0f4bebf0d76957faf82a35","schema_version":"1.0","event_id":"sha256:b835c6c78ed3e8bb0f0a55a6bb8d2a25d58de7469b0f4bebf0d76957faf82a35"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MYBHC33FG7E2QJFIUIEIRWJWH5/bundle.json","state_url":"https://pith.science/pith/MYBHC33FG7E2QJFIUIEIRWJWH5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MYBHC33FG7E2QJFIUIEIRWJWH5/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":"2026-05-30T05:26:35Z","links":{"resolver":"https://pith.science/pith/MYBHC33FG7E2QJFIUIEIRWJWH5","bundle":"https://pith.science/pith/MYBHC33FG7E2QJFIUIEIRWJWH5/bundle.json","state":"https://pith.science/pith/MYBHC33FG7E2QJFIUIEIRWJWH5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MYBHC33FG7E2QJFIUIEIRWJWH5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:MYBHC33FG7E2QJFIUIEIRWJWH5","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":"1c42d1ca559a9d151f2d25c56572cff58ed6bbc1bd2474c15fb50c5532a54815","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-07-05T18:56:14Z","title_canon_sha256":"15697fc6c1ffb9f55d0d8ed533ad61a0645065c1a03ee5f0ec8eb99f5670a9e4"},"schema_version":"1.0","source":{"id":"1807.02152","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.02152","created_at":"2026-05-18T00:11:22Z"},{"alias_kind":"arxiv_version","alias_value":"1807.02152v1","created_at":"2026-05-18T00:11:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02152","created_at":"2026-05-18T00:11:22Z"},{"alias_kind":"pith_short_12","alias_value":"MYBHC33FG7E2","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_16","alias_value":"MYBHC33FG7E2QJFI","created_at":"2026-05-18T12:32:40Z"},{"alias_kind":"pith_short_8","alias_value":"MYBHC33F","created_at":"2026-05-18T12:32:40Z"}],"graph_snapshots":[{"event_id":"sha256:b835c6c78ed3e8bb0f0a55a6bb8d2a25d58de7469b0f4bebf0d76957faf82a35","target":"graph","created_at":"2026-05-18T00:11:22Z","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":"Objective: To develop an automatic image normalization algorithm for intensity correction of images from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired by different MRI scanners with various imaging parameters, using only image information. Methods: DCE-MR images of 460 subjects with breast cancer acquired by different scanners were used in this study. Each subject had one T1-weighted pre-contrast image and three T1-weighted post-contrast images available. Our normalization algorithm operated under the assumption that the same type of tissue in different patient","authors_text":"Ashirbani Saha, Brian J. Soher, Jun Zhang, Maciej A. Mazurowski","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-07-05T18:56:14Z","title":"Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02152","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":"sha256:005e360487f176d4c3f42e8ad4301c02d233cbf9c6a36cfaa2eaad8cdb0ed770","target":"record","created_at":"2026-05-18T00:11:22Z","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":"1c42d1ca559a9d151f2d25c56572cff58ed6bbc1bd2474c15fb50c5532a54815","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2018-07-05T18:56:14Z","title_canon_sha256":"15697fc6c1ffb9f55d0d8ed533ad61a0645065c1a03ee5f0ec8eb99f5670a9e4"},"schema_version":"1.0","source":{"id":"1807.02152","kind":"arxiv","version":1}},"canonical_sha256":"6602716f6537c9a824a8a20888d9363f7ad56100ec63f096177991369730a85d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6602716f6537c9a824a8a20888d9363f7ad56100ec63f096177991369730a85d","first_computed_at":"2026-05-18T00:11:22.964844Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:22.964844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"e/kJS9qeLzGthnvm2G1aK2LxzGkPIy4tfJKeKQptCPdpu+AahDdIzfJuEe76wQHs6ctTXQmCmWwbFYxoAUgqDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:22.965412Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.02152","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:005e360487f176d4c3f42e8ad4301c02d233cbf9c6a36cfaa2eaad8cdb0ed770","sha256:b835c6c78ed3e8bb0f0a55a6bb8d2a25d58de7469b0f4bebf0d76957faf82a35"],"state_sha256":"1e72e2259b4fac856ee59b04ce61ec0d3a98de8b24e52b1793957a5b68ed8f56"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/eF9Rjmw5aykoq77KAAowUoldvj13oX29IIc0uEm7QjVYaH7WXXerjgqUT+GCVLDvg/jiPvD0rJ4shvp9HsIDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T05:26:35.593701Z","bundle_sha256":"a99dca6a912c5dc1e008e245c9f4ccd7ecbb504b352479fe6ace550190f42734"}}