{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:RIANJVCMWC45YHSTIZSBNG63EB","short_pith_number":"pith:RIANJVCM","canonical_record":{"source":{"id":"2205.01604","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-05-03T16:33:10Z","cross_cats_sorted":[],"title_canon_sha256":"db8278394f1eb4533d43d8ce4e8df3826ef96dc4f0941ad43bfadaa689858638","abstract_canon_sha256":"dee63c10560f0162e7fded22aa8cec954c7f7a9817c8ba51a87dad48354de90e"},"schema_version":"1.0"},"canonical_sha256":"8a00d4d44cb0b9dc1e534664169bdb204af4595dac7566d99c06c2a32b025bd5","source":{"kind":"arxiv","id":"2205.01604","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.01604","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"arxiv_version","alias_value":"2205.01604v3","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.01604","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"pith_short_12","alias_value":"RIANJVCMWC45","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"pith_short_16","alias_value":"RIANJVCMWC45YHST","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"pith_short_8","alias_value":"RIANJVCM","created_at":"2026-07-05T05:21:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:RIANJVCMWC45YHSTIZSBNG63EB","target":"record","payload":{"canonical_record":{"source":{"id":"2205.01604","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-05-03T16:33:10Z","cross_cats_sorted":[],"title_canon_sha256":"db8278394f1eb4533d43d8ce4e8df3826ef96dc4f0941ad43bfadaa689858638","abstract_canon_sha256":"dee63c10560f0162e7fded22aa8cec954c7f7a9817c8ba51a87dad48354de90e"},"schema_version":"1.0"},"canonical_sha256":"8a00d4d44cb0b9dc1e534664169bdb204af4595dac7566d99c06c2a32b025bd5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:21:25.273590Z","signature_b64":"G/tgGtV8lAKhAHp/t7JZz1WqdwzL2NCXJ2tewqrK5vQH2/0dYEcr+5Fj1S9v0CP8x+GMnW7p+SXusqb50IDFCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a00d4d44cb0b9dc1e534664169bdb204af4595dac7566d99c06c2a32b025bd5","last_reissued_at":"2026-07-05T05:21:25.272984Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:21:25.272984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.01604","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:21:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tEgXoeEZsduaK4nW4Toq3vs0efNUwPI1KfvF9mL6bOIRt+k2blL39hoW3mR2uI8Gf1L8VI0K6M+ErRHX6gMSDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T22:48:10.525714Z"},"content_sha256":"7832dd265d80b6cb31ea9d08b3ae0372db4878fe7b4998d839e351e2a5cb3186","schema_version":"1.0","event_id":"sha256:7832dd265d80b6cb31ea9d08b3ae0372db4878fe7b4998d839e351e2a5cb3186"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:RIANJVCMWC45YHSTIZSBNG63EB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Chengyue Wu, John Virostko, Jonathan I. Tamir, Julie C. DiCarlo, Kalina P. Slavkova, Sidharth Kumar, Thomas E. Yankeelov, Viraj Wadhwa","submitted_at":"2022-05-03T16:33:10Z","abstract_excerpt":"The purpose of this work is to implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data. The ConvDecoder neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle (VFA) data. Fully-sampled VFA k-space data were retrospectively accelerated by factors of R={8,12,18,36} and reconstructed with ConvDecoder (CD), ConvDecoder with the proposed regularization (CD+r), locally low-rank (LR) reconstruction, and co"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.01604","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2205.01604/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T05:21:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WsIXEI+v7HSM58E1NzqHnVjEnsQeh1BiXgo4GjlCqQu/OQdoSutEwPi52CXrDQSk238fQ0hpVv7wtIWFE++ZAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T22:48:10.526227Z"},"content_sha256":"d2ca29a4bf87554e30ad070bfce96efe1061446b87ac1dcf715de87d84574698","schema_version":"1.0","event_id":"sha256:d2ca29a4bf87554e30ad070bfce96efe1061446b87ac1dcf715de87d84574698"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RIANJVCMWC45YHSTIZSBNG63EB/bundle.json","state_url":"https://pith.science/pith/RIANJVCMWC45YHSTIZSBNG63EB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RIANJVCMWC45YHSTIZSBNG63EB/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":"2026-08-23T22:48:10Z","links":{"resolver":"https://pith.science/pith/RIANJVCMWC45YHSTIZSBNG63EB","bundle":"https://pith.science/pith/RIANJVCMWC45YHSTIZSBNG63EB/bundle.json","state":"https://pith.science/pith/RIANJVCMWC45YHSTIZSBNG63EB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RIANJVCMWC45YHSTIZSBNG63EB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:RIANJVCMWC45YHSTIZSBNG63EB","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":"dee63c10560f0162e7fded22aa8cec954c7f7a9817c8ba51a87dad48354de90e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-05-03T16:33:10Z","title_canon_sha256":"db8278394f1eb4533d43d8ce4e8df3826ef96dc4f0941ad43bfadaa689858638"},"schema_version":"1.0","source":{"id":"2205.01604","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.01604","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"arxiv_version","alias_value":"2205.01604v3","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.01604","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"pith_short_12","alias_value":"RIANJVCMWC45","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"pith_short_16","alias_value":"RIANJVCMWC45YHST","created_at":"2026-07-05T05:21:25Z"},{"alias_kind":"pith_short_8","alias_value":"RIANJVCM","created_at":"2026-07-05T05:21:25Z"}],"graph_snapshots":[{"event_id":"sha256:d2ca29a4bf87554e30ad070bfce96efe1061446b87ac1dcf715de87d84574698","target":"graph","created_at":"2026-07-05T05:21:25Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2205.01604/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The purpose of this work is to implement physics-based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data. The ConvDecoder neural network was trained with a physics-based regularization term incorporating the spoiled gradient echo equation that describes variable-flip angle (VFA) data. Fully-sampled VFA k-space data were retrospectively accelerated by factors of R={8,12,18,36} and reconstructed with ConvDecoder (CD), ConvDecoder with the proposed regularization (CD+r), locally low-rank (LR) reconstruction, and co","authors_text":"Chengyue Wu, John Virostko, Jonathan I. Tamir, Julie C. DiCarlo, Kalina P. Slavkova, Sidharth Kumar, Thomas E. Yankeelov, Viraj Wadhwa","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-05-03T16:33:10Z","title":"An untrained deep learning method for reconstructing dynamic magnetic resonance images from accelerated model-based data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.01604","kind":"arxiv","version":3},"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":"sha256:7832dd265d80b6cb31ea9d08b3ae0372db4878fe7b4998d839e351e2a5cb3186","target":"record","created_at":"2026-07-05T05:21:25Z","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":"dee63c10560f0162e7fded22aa8cec954c7f7a9817c8ba51a87dad48354de90e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2022-05-03T16:33:10Z","title_canon_sha256":"db8278394f1eb4533d43d8ce4e8df3826ef96dc4f0941ad43bfadaa689858638"},"schema_version":"1.0","source":{"id":"2205.01604","kind":"arxiv","version":3}},"canonical_sha256":"8a00d4d44cb0b9dc1e534664169bdb204af4595dac7566d99c06c2a32b025bd5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8a00d4d44cb0b9dc1e534664169bdb204af4595dac7566d99c06c2a32b025bd5","first_computed_at":"2026-07-05T05:21:25.272984Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:21:25.272984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"G/tgGtV8lAKhAHp/t7JZz1WqdwzL2NCXJ2tewqrK5vQH2/0dYEcr+5Fj1S9v0CP8x+GMnW7p+SXusqb50IDFCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:21:25.273590Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.01604","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7832dd265d80b6cb31ea9d08b3ae0372db4878fe7b4998d839e351e2a5cb3186","sha256:d2ca29a4bf87554e30ad070bfce96efe1061446b87ac1dcf715de87d84574698"],"state_sha256":"c537ab4cc8c056150b64e7ef9b54fe579837358eec70587742712b90a2c6f8f7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PgtNOeKMPSGoFLmiMj6UWleGlNYBYwjSMr5m0yKhKLYCuR8M9mv0whPw0yG+MQk9OAi+6Aa44RGAsRA5icLZBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T22:48:10.532886Z","bundle_sha256":"74a82347edebd3b3b5339b394ef44ddd1989bc48591351a56b2f6e16b35158a3"}}