{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:O736PDWRJO3BJTAJWNAW7Q2PHZ","short_pith_number":"pith:O736PDWR","canonical_record":{"source":{"id":"1501.01898","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-01-08T16:29:16Z","cross_cats_sorted":["stat.AP","stat.ME"],"title_canon_sha256":"bfad01223173c092a27d36a46d1da7cdfd77682afc2679bdfbc87dba0d2d94f7","abstract_canon_sha256":"f70dfb9bcddb4abe06f92e1c233bb7843459620d4f5a915835a2e67860af2be8"},"schema_version":"1.0"},"canonical_sha256":"77f7e78ed14bb614cc09b3416fc34f3e6945f4742458860b29f6197629a89b15","source":{"kind":"arxiv","id":"1501.01898","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1501.01898","created_at":"2026-05-18T02:29:46Z"},{"alias_kind":"arxiv_version","alias_value":"1501.01898v1","created_at":"2026-05-18T02:29:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.01898","created_at":"2026-05-18T02:29:46Z"},{"alias_kind":"pith_short_12","alias_value":"O736PDWRJO3B","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"O736PDWRJO3BJTAJ","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"O736PDWR","created_at":"2026-05-18T12:29:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:O736PDWRJO3BJTAJWNAW7Q2PHZ","target":"record","payload":{"canonical_record":{"source":{"id":"1501.01898","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-01-08T16:29:16Z","cross_cats_sorted":["stat.AP","stat.ME"],"title_canon_sha256":"bfad01223173c092a27d36a46d1da7cdfd77682afc2679bdfbc87dba0d2d94f7","abstract_canon_sha256":"f70dfb9bcddb4abe06f92e1c233bb7843459620d4f5a915835a2e67860af2be8"},"schema_version":"1.0"},"canonical_sha256":"77f7e78ed14bb614cc09b3416fc34f3e6945f4742458860b29f6197629a89b15","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:29:46.097245Z","signature_b64":"OMLGaBwbdZN+YPoBa26NBi+K3jhorYDE6Lzro1gX1wBDqg/idNdK1a/tHjAWONtjwmYAsfcWml/LpKc+zXDyAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77f7e78ed14bb614cc09b3416fc34f3e6945f4742458860b29f6197629a89b15","last_reissued_at":"2026-05-18T02:29:46.096818Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:29:46.096818Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1501.01898","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":"2026-05-18T02:29:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sB2RaT5qA61QjoyR4uLfUYEfZiU2k3Llh4IUc94kIDhjlPP74h7D8K72cRzbwIsV6/5w92sSGFovUyerfFpxAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T04:54:10.619427Z"},"content_sha256":"9bc44e1ece32381943012bb62846a2dd6b1614490dafe5f0ea0f28b523383291","schema_version":"1.0","event_id":"sha256:9bc44e1ece32381943012bb62846a2dd6b1614490dafe5f0ea0f28b523383291"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:O736PDWRJO3BJTAJWNAW7Q2PHZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast Estimation of Diffusion Tensors under Rician noise by the EM algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP","stat.ME"],"primary_cat":"stat.CO","authors_text":"Dario Gasbarra, Jia Liu, Juha Railavo","submitted_at":"2015-01-08T16:29:16Z","abstract_excerpt":"This paper presents a fast computational method, the Expectation Maximization algorithm, for Maximum Likelihood (ML) estimation in diffusion tensor imaging under the Rice noise model. We further extend the ML framework to the maximum a posterior (MAP) estimation and describe the numerical similarities of both ML and MAP estimators. This novel method is implemented and applied using both synthetic and real data in a wide range of b amplitudes. The comparison with other popular methods are made in accuracy, methodology and computation."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.01898","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":"2026-05-18T02:29:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fU52QEjzw/m+zka0kwOsNMp4+v5G4fuHH4R+Xq/szpa9g6G8oj1UkukwZp6/J7cwe2aFPJ2fiK8nEZcAFvCPBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T04:54:10.620130Z"},"content_sha256":"f2f9f3af76961969582b1d700eb4435424785fcdd08654e85c3087b04c1496f0","schema_version":"1.0","event_id":"sha256:f2f9f3af76961969582b1d700eb4435424785fcdd08654e85c3087b04c1496f0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O736PDWRJO3BJTAJWNAW7Q2PHZ/bundle.json","state_url":"https://pith.science/pith/O736PDWRJO3BJTAJWNAW7Q2PHZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O736PDWRJO3BJTAJWNAW7Q2PHZ/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":"2026-06-09T04:54:10Z","links":{"resolver":"https://pith.science/pith/O736PDWRJO3BJTAJWNAW7Q2PHZ","bundle":"https://pith.science/pith/O736PDWRJO3BJTAJWNAW7Q2PHZ/bundle.json","state":"https://pith.science/pith/O736PDWRJO3BJTAJWNAW7Q2PHZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O736PDWRJO3BJTAJWNAW7Q2PHZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:O736PDWRJO3BJTAJWNAW7Q2PHZ","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":"f70dfb9bcddb4abe06f92e1c233bb7843459620d4f5a915835a2e67860af2be8","cross_cats_sorted":["stat.AP","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-01-08T16:29:16Z","title_canon_sha256":"bfad01223173c092a27d36a46d1da7cdfd77682afc2679bdfbc87dba0d2d94f7"},"schema_version":"1.0","source":{"id":"1501.01898","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1501.01898","created_at":"2026-05-18T02:29:46Z"},{"alias_kind":"arxiv_version","alias_value":"1501.01898v1","created_at":"2026-05-18T02:29:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1501.01898","created_at":"2026-05-18T02:29:46Z"},{"alias_kind":"pith_short_12","alias_value":"O736PDWRJO3B","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"O736PDWRJO3BJTAJ","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"O736PDWR","created_at":"2026-05-18T12:29:34Z"}],"graph_snapshots":[{"event_id":"sha256:f2f9f3af76961969582b1d700eb4435424785fcdd08654e85c3087b04c1496f0","target":"graph","created_at":"2026-05-18T02:29:46Z","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":"This paper presents a fast computational method, the Expectation Maximization algorithm, for Maximum Likelihood (ML) estimation in diffusion tensor imaging under the Rice noise model. We further extend the ML framework to the maximum a posterior (MAP) estimation and describe the numerical similarities of both ML and MAP estimators. This novel method is implemented and applied using both synthetic and real data in a wide range of b amplitudes. The comparison with other popular methods are made in accuracy, methodology and computation.","authors_text":"Dario Gasbarra, Jia Liu, Juha Railavo","cross_cats":["stat.AP","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-01-08T16:29:16Z","title":"Fast Estimation of Diffusion Tensors under Rician noise by the EM algorithm"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1501.01898","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":"sha256:9bc44e1ece32381943012bb62846a2dd6b1614490dafe5f0ea0f28b523383291","target":"record","created_at":"2026-05-18T02:29:46Z","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":"f70dfb9bcddb4abe06f92e1c233bb7843459620d4f5a915835a2e67860af2be8","cross_cats_sorted":["stat.AP","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-01-08T16:29:16Z","title_canon_sha256":"bfad01223173c092a27d36a46d1da7cdfd77682afc2679bdfbc87dba0d2d94f7"},"schema_version":"1.0","source":{"id":"1501.01898","kind":"arxiv","version":1}},"canonical_sha256":"77f7e78ed14bb614cc09b3416fc34f3e6945f4742458860b29f6197629a89b15","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"77f7e78ed14bb614cc09b3416fc34f3e6945f4742458860b29f6197629a89b15","first_computed_at":"2026-05-18T02:29:46.096818Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:29:46.096818Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OMLGaBwbdZN+YPoBa26NBi+K3jhorYDE6Lzro1gX1wBDqg/idNdK1a/tHjAWONtjwmYAsfcWml/LpKc+zXDyAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:29:46.097245Z","signed_message":"canonical_sha256_bytes"},"source_id":"1501.01898","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9bc44e1ece32381943012bb62846a2dd6b1614490dafe5f0ea0f28b523383291","sha256:f2f9f3af76961969582b1d700eb4435424785fcdd08654e85c3087b04c1496f0"],"state_sha256":"3fcb64d3b33dabfc1f037967cc4681ea1c602d2201af52f835f915837ee8fd6e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/hVazpZLKWkriMDZBgdiWQEIAwLOiNSn1frrlJKA49KL05vFRsghe0oteh5xgJdgS17xk8YaiaLv8SthF5lOBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T04:54:10.623691Z","bundle_sha256":"473094be3514cc0e6bba1b294c553b1ce05ef34453463d097eefca0f6142b1d4"}}