{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:XMB7ONMRYL4CQB3UXCTQOGTGEC","short_pith_number":"pith:XMB7ONMR","canonical_record":{"source":{"id":"1809.03308","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-02T21:43:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"85b41273bf22c4df2a187974fdb97b13f64e01b8e2d17c7cdee8434b9f391055","abstract_canon_sha256":"0033b07a5ef01f52659132e572be4362650089f2e576c8f089e08525d4b8c0e4"},"schema_version":"1.0"},"canonical_sha256":"bb03f73591c2f8280774b8a7071a66209e1d1709e7bf839f1849f03bf136cd46","source":{"kind":"arxiv","id":"1809.03308","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.03308","created_at":"2026-05-18T00:06:09Z"},{"alias_kind":"arxiv_version","alias_value":"1809.03308v1","created_at":"2026-05-18T00:06:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.03308","created_at":"2026-05-18T00:06:09Z"},{"alias_kind":"pith_short_12","alias_value":"XMB7ONMRYL4C","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"XMB7ONMRYL4CQB3U","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"XMB7ONMR","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:XMB7ONMRYL4CQB3UXCTQOGTGEC","target":"record","payload":{"canonical_record":{"source":{"id":"1809.03308","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-02T21:43:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"85b41273bf22c4df2a187974fdb97b13f64e01b8e2d17c7cdee8434b9f391055","abstract_canon_sha256":"0033b07a5ef01f52659132e572be4362650089f2e576c8f089e08525d4b8c0e4"},"schema_version":"1.0"},"canonical_sha256":"bb03f73591c2f8280774b8a7071a66209e1d1709e7bf839f1849f03bf136cd46","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:09.257223Z","signature_b64":"Byi83zFpWiDvb+QV2AxCeaeSKDuaVdVCyXGvbXQaSqP27tIZGng2G2+H0zFKAjMXuauRU1YLATACCc/sYDPvBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb03f73591c2f8280774b8a7071a66209e1d1709e7bf839f1849f03bf136cd46","last_reissued_at":"2026-05-18T00:06:09.256639Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:09.256639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1809.03308","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":"2026-05-18T00:06:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8321k8OfFWCsMYKPhynRFZYUU+W8/vLwPmtdofyGS+sU/YwgN12sjwgAcwlwXHlnLv2hLsNNZQTuzQhJVIoCDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T15:21:52.271661Z"},"content_sha256":"351a81999d260d615d15bbff5d09f622a3c82381c552072d4ce123c78cea23f8","schema_version":"1.0","event_id":"sha256:351a81999d260d615d15bbff5d09f622a3c82381c552072d4ce123c78cea23f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:XMB7ONMRYL4CQB3UXCTQOGTGEC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for efficient MR T2 mapping","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Fang Liu, Li Feng, Richard Kijowski","submitted_at":"2018-09-02T21:43:49Z","abstract_excerpt":"Quantitative mapping of magnetic resonance (MR) parameters have been shown as valuable methods for improved assessment of a range of diseases. Due to the need to image an anatomic structure multiple times, parameter mapping usually requires long scan times compared to conventional static imaging. Therefore, accelerated parameter mapping is highly-desirable and remains a topic of great interest in the MR research community. While many recent deep learning methods have focused on highly efficient image reconstruction for conventional static MR imaging, applications of deep learning for dynamic i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.03308","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":"2026-05-18T00:06:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"F50PKSsnPm8YDWezBAjovMa4UJnXuv3Jkno1n0APGzLADsOjPGyUhvnJdpiRGsbgme0DsVm5a3q8XvvaBeNrCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T15:21:52.272414Z"},"content_sha256":"d43ac31b9ddfb2339d94858a5c2bd1328d12d94ad4a381d993c3d7101d4de743","schema_version":"1.0","event_id":"sha256:d43ac31b9ddfb2339d94858a5c2bd1328d12d94ad4a381d993c3d7101d4de743"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XMB7ONMRYL4CQB3UXCTQOGTGEC/bundle.json","state_url":"https://pith.science/pith/XMB7ONMRYL4CQB3UXCTQOGTGEC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XMB7ONMRYL4CQB3UXCTQOGTGEC/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":"2026-05-25T15:21:52Z","links":{"resolver":"https://pith.science/pith/XMB7ONMRYL4CQB3UXCTQOGTGEC","bundle":"https://pith.science/pith/XMB7ONMRYL4CQB3UXCTQOGTGEC/bundle.json","state":"https://pith.science/pith/XMB7ONMRYL4CQB3UXCTQOGTGEC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XMB7ONMRYL4CQB3UXCTQOGTGEC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:XMB7ONMRYL4CQB3UXCTQOGTGEC","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":"0033b07a5ef01f52659132e572be4362650089f2e576c8f089e08525d4b8c0e4","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-02T21:43:49Z","title_canon_sha256":"85b41273bf22c4df2a187974fdb97b13f64e01b8e2d17c7cdee8434b9f391055"},"schema_version":"1.0","source":{"id":"1809.03308","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1809.03308","created_at":"2026-05-18T00:06:09Z"},{"alias_kind":"arxiv_version","alias_value":"1809.03308v1","created_at":"2026-05-18T00:06:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.03308","created_at":"2026-05-18T00:06:09Z"},{"alias_kind":"pith_short_12","alias_value":"XMB7ONMRYL4C","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"XMB7ONMRYL4CQB3U","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"XMB7ONMR","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:d43ac31b9ddfb2339d94858a5c2bd1328d12d94ad4a381d993c3d7101d4de743","target":"graph","created_at":"2026-05-18T00:06:09Z","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":"Quantitative mapping of magnetic resonance (MR) parameters have been shown as valuable methods for improved assessment of a range of diseases. Due to the need to image an anatomic structure multiple times, parameter mapping usually requires long scan times compared to conventional static imaging. Therefore, accelerated parameter mapping is highly-desirable and remains a topic of great interest in the MR research community. While many recent deep learning methods have focused on highly efficient image reconstruction for conventional static MR imaging, applications of deep learning for dynamic i","authors_text":"Fang Liu, Li Feng, Richard Kijowski","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-02T21:43:49Z","title":"MANTIS: Model-Augmented Neural neTwork with Incoherent k-space Sampling for efficient MR T2 mapping"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.03308","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":"sha256:351a81999d260d615d15bbff5d09f622a3c82381c552072d4ce123c78cea23f8","target":"record","created_at":"2026-05-18T00:06:09Z","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":"0033b07a5ef01f52659132e572be4362650089f2e576c8f089e08525d4b8c0e4","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-02T21:43:49Z","title_canon_sha256":"85b41273bf22c4df2a187974fdb97b13f64e01b8e2d17c7cdee8434b9f391055"},"schema_version":"1.0","source":{"id":"1809.03308","kind":"arxiv","version":1}},"canonical_sha256":"bb03f73591c2f8280774b8a7071a66209e1d1709e7bf839f1849f03bf136cd46","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bb03f73591c2f8280774b8a7071a66209e1d1709e7bf839f1849f03bf136cd46","first_computed_at":"2026-05-18T00:06:09.256639Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:09.256639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Byi83zFpWiDvb+QV2AxCeaeSKDuaVdVCyXGvbXQaSqP27tIZGng2G2+H0zFKAjMXuauRU1YLATACCc/sYDPvBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:09.257223Z","signed_message":"canonical_sha256_bytes"},"source_id":"1809.03308","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:351a81999d260d615d15bbff5d09f622a3c82381c552072d4ce123c78cea23f8","sha256:d43ac31b9ddfb2339d94858a5c2bd1328d12d94ad4a381d993c3d7101d4de743"],"state_sha256":"c82a217b5fe16f953edd498372fb112ebbb4e073b93a5fb90bf9b47b66e8c618"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YzAke+7s5iR33u7IKkkz1DFEXSvMaQLtKde3FNVzOdAn7oUGjg6zgU+oAIwEAZIp6lV1KPGAhiR2qE/0itNNBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T15:21:52.276358Z","bundle_sha256":"4401e44826485524bb0940585c7a73a2c15d4f63223557cb1c772679e6e5d656"}}