{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:BTSSPECYUOS2HMALAH6ERDTGJB","short_pith_number":"pith:BTSSPECY","schema_version":"1.0","canonical_sha256":"0ce5279058a3a5a3b00b01fc488e6648450ee41197d0788f94fe342fd9958241","source":{"kind":"arxiv","id":"1912.09278","version":1},"attestation_state":"computed","paper":{"title":"$\\Sigma$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Chen Qin, Daniel Rueckert, Jinming Duan, Jo Schlemper, Kerstin Hammernik, Ronald M. Summers","submitted_at":"2019-12-18T16:52:39Z","abstract_excerpt":"Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a $\\Sigma$-net, for accelerated parallel MR image reconstruction using deep learning.\n  Theory and Methods: MR image reconstruction is formulated as learned unrolled optimization scheme with a Down-Up network as regularization and varying data consistency layers. The different architectures are split into sensitivity networks, which rely on explicit coil sensitivity maps, and parallel coil networks, which learn the combination of coils implic"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1912.09278","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-12-18T16:52:39Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"aa38290d6f9fc4e0efe399c47282e0c1c6fc9c1bbd386d4a7aaf7e0618d60356","abstract_canon_sha256":"074f0648ff6cd8905693685eb3bb5ca163ed400e43446e328b08a722aaeec27e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:27:25.261130Z","signature_b64":"yPk8aTiIh8jLEFlcUKW9Sa9IBZHhg2yawD5u6PxuFnVO3NUjwN7nj41rUvwOT/JNwV5t1KShpZrDi5KgwPm0Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ce5279058a3a5a3b00b01fc488e6648450ee41197d0788f94fe342fd9958241","last_reissued_at":"2026-07-05T00:27:25.260695Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:27:25.260695Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"$\\Sigma$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Chen Qin, Daniel Rueckert, Jinming Duan, Jo Schlemper, Kerstin Hammernik, Ronald M. Summers","submitted_at":"2019-12-18T16:52:39Z","abstract_excerpt":"Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a $\\Sigma$-net, for accelerated parallel MR image reconstruction using deep learning.\n  Theory and Methods: MR image reconstruction is formulated as learned unrolled optimization scheme with a Down-Up network as regularization and varying data consistency layers. The different architectures are split into sensitivity networks, which rely on explicit coil sensitivity maps, and parallel coil networks, which learn the combination of coils implic"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.09278","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1912.09278/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1912.09278","created_at":"2026-07-05T00:27:25.260761+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.09278v1","created_at":"2026-07-05T00:27:25.260761+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.09278","created_at":"2026-07-05T00:27:25.260761+00:00"},{"alias_kind":"pith_short_12","alias_value":"BTSSPECYUOS2","created_at":"2026-07-05T00:27:25.260761+00:00"},{"alias_kind":"pith_short_16","alias_value":"BTSSPECYUOS2HMAL","created_at":"2026-07-05T00:27:25.260761+00:00"},{"alias_kind":"pith_short_8","alias_value":"BTSSPECY","created_at":"2026-07-05T00:27:25.260761+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.11762","citing_title":"MosaicMRI: A Diverse Dataset and Benchmark for Raw Musculoskeletal MRI","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BTSSPECYUOS2HMALAH6ERDTGJB","json":"https://pith.science/pith/BTSSPECYUOS2HMALAH6ERDTGJB.json","graph_json":"https://pith.science/api/pith-number/BTSSPECYUOS2HMALAH6ERDTGJB/graph.json","events_json":"https://pith.science/api/pith-number/BTSSPECYUOS2HMALAH6ERDTGJB/events.json","paper":"https://pith.science/paper/BTSSPECY"},"agent_actions":{"view_html":"https://pith.science/pith/BTSSPECYUOS2HMALAH6ERDTGJB","download_json":"https://pith.science/pith/BTSSPECYUOS2HMALAH6ERDTGJB.json","view_paper":"https://pith.science/paper/BTSSPECY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.09278&json=true","fetch_graph":"https://pith.science/api/pith-number/BTSSPECYUOS2HMALAH6ERDTGJB/graph.json","fetch_events":"https://pith.science/api/pith-number/BTSSPECYUOS2HMALAH6ERDTGJB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BTSSPECYUOS2HMALAH6ERDTGJB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BTSSPECYUOS2HMALAH6ERDTGJB/action/storage_attestation","attest_author":"https://pith.science/pith/BTSSPECYUOS2HMALAH6ERDTGJB/action/author_attestation","sign_citation":"https://pith.science/pith/BTSSPECYUOS2HMALAH6ERDTGJB/action/citation_signature","submit_replication":"https://pith.science/pith/BTSSPECYUOS2HMALAH6ERDTGJB/action/replication_record"}},"created_at":"2026-07-05T00:27:25.260761+00:00","updated_at":"2026-07-05T00:27:25.260761+00:00"}