{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:3DC6TRRBWECMALTE37UKY3F643","short_pith_number":"pith:3DC6TRRB","schema_version":"1.0","canonical_sha256":"d8c5e9c621b104c02e64dfe8ac6cbee6d1e2c73df18ef4e7acd947748317d48d","source":{"kind":"arxiv","id":"1707.00070","version":1},"attestation_state":"computed","paper":{"title":"Better than Real: Complex-valued Neural Nets for MRI Fingerprinting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Michael Lustig, Patrick Virtue, Stella X. Yu","submitted_at":"2017-07-01T00:15:33Z","abstract_excerpt":"The task of MRI fingerprinting is to identify tissue parameters from complex-valued MRI signals. The prevalent approach is dictionary based, where a test MRI signal is compared to stored MRI signals with known tissue parameters and the most similar signals and tissue parameters retrieved. Such an approach does not scale with the number of parameters and is rather slow when the tissue parameter space is large.\n  Our first novel contribution is to use deep learning as an efficient nonlinear inverse mapping approach. We generate synthetic (tissue, MRI) data from an MRI simulator, and use them to "},"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":"1707.00070","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-01T00:15:33Z","cross_cats_sorted":[],"title_canon_sha256":"c14d8f406770cee6c8f11c305ad0b110121641b6f737a7077622396ef24b722f","abstract_canon_sha256":"4042aeb8e5a93057ba82e634536fc45d5e4d6d631fb209ca83bf1fe5564804cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:41:06.575334Z","signature_b64":"IkfX0ZM1He7Oof2UG658SQQ5dxNh5ftiryhhK9m8bqGbm+XvP6xFzy8rUgOR2cdYDj4na5TKY0w0tXnbvjghBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8c5e9c621b104c02e64dfe8ac6cbee6d1e2c73df18ef4e7acd947748317d48d","last_reissued_at":"2026-05-18T00:41:06.574765Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:41:06.574765Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Better than Real: Complex-valued Neural Nets for MRI Fingerprinting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Michael Lustig, Patrick Virtue, Stella X. Yu","submitted_at":"2017-07-01T00:15:33Z","abstract_excerpt":"The task of MRI fingerprinting is to identify tissue parameters from complex-valued MRI signals. The prevalent approach is dictionary based, where a test MRI signal is compared to stored MRI signals with known tissue parameters and the most similar signals and tissue parameters retrieved. Such an approach does not scale with the number of parameters and is rather slow when the tissue parameter space is large.\n  Our first novel contribution is to use deep learning as an efficient nonlinear inverse mapping approach. We generate synthetic (tissue, MRI) data from an MRI simulator, and use them to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.00070","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1707.00070","created_at":"2026-05-18T00:41:06.574861+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.00070v1","created_at":"2026-05-18T00:41:06.574861+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.00070","created_at":"2026-05-18T00:41:06.574861+00:00"},{"alias_kind":"pith_short_12","alias_value":"3DC6TRRBWECM","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_16","alias_value":"3DC6TRRBWECMALTE","created_at":"2026-05-18T12:30:58.224056+00:00"},{"alias_kind":"pith_short_8","alias_value":"3DC6TRRB","created_at":"2026-05-18T12:30:58.224056+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3DC6TRRBWECMALTE37UKY3F643","json":"https://pith.science/pith/3DC6TRRBWECMALTE37UKY3F643.json","graph_json":"https://pith.science/api/pith-number/3DC6TRRBWECMALTE37UKY3F643/graph.json","events_json":"https://pith.science/api/pith-number/3DC6TRRBWECMALTE37UKY3F643/events.json","paper":"https://pith.science/paper/3DC6TRRB"},"agent_actions":{"view_html":"https://pith.science/pith/3DC6TRRBWECMALTE37UKY3F643","download_json":"https://pith.science/pith/3DC6TRRBWECMALTE37UKY3F643.json","view_paper":"https://pith.science/paper/3DC6TRRB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.00070&json=true","fetch_graph":"https://pith.science/api/pith-number/3DC6TRRBWECMALTE37UKY3F643/graph.json","fetch_events":"https://pith.science/api/pith-number/3DC6TRRBWECMALTE37UKY3F643/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3DC6TRRBWECMALTE37UKY3F643/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3DC6TRRBWECMALTE37UKY3F643/action/storage_attestation","attest_author":"https://pith.science/pith/3DC6TRRBWECMALTE37UKY3F643/action/author_attestation","sign_citation":"https://pith.science/pith/3DC6TRRBWECMALTE37UKY3F643/action/citation_signature","submit_replication":"https://pith.science/pith/3DC6TRRBWECMALTE37UKY3F643/action/replication_record"}},"created_at":"2026-05-18T00:41:06.574861+00:00","updated_at":"2026-05-18T00:41:06.574861+00:00"}