{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:GKIQR7ENFONWKLK3GDW4HBPLOK","short_pith_number":"pith:GKIQR7EN","schema_version":"1.0","canonical_sha256":"329108fc8d2b9b652d5b30edc385eb7295267bba222a64cceda91e6b6e48611f","source":{"kind":"arxiv","id":"2101.12366","version":1},"attestation_state":"computed","paper":{"title":"Deep Generative SToRM model for dynamic imaging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Abdul Haseeb Ahmed, Mathews Jacob, Prashant Nagpal, Qing Zou, Stanley Kruger","submitted_at":"2021-01-29T02:35:57Z","abstract_excerpt":"We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed generative framework represents the image time series as a smooth non-linear function of low-dimensional latent vectors that capture the cardiac and respiratory phases. The non-linear function is represented using a deep convolutional neural network (CNN). Unlike the popular CNN approaches that require extensive fully-sampled training data that is not available in this setting, the parameters of the CNN generator as well"},"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":"2101.12366","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2021-01-29T02:35:57Z","cross_cats_sorted":[],"title_canon_sha256":"86f799a85bbc087fb28b840821cef95542f37c6fcc1dad39abe8c2e01924974d","abstract_canon_sha256":"bfd69b008fc5d65d5a72e9da3975512cfb16c0874be27a7b0fd262adf0659317"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:10:43.120092Z","signature_b64":"1OwOcfzD8V6DG5nWUPP1oV6J32czwR6qxJAcPr/DxaDjgvpIuuwMDRwvqItvc4GHX/8zP9vrICh4STp4oCg4BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"329108fc8d2b9b652d5b30edc385eb7295267bba222a64cceda91e6b6e48611f","last_reissued_at":"2026-07-05T02:10:43.119738Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:10:43.119738Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Generative SToRM model for dynamic imaging","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Abdul Haseeb Ahmed, Mathews Jacob, Prashant Nagpal, Qing Zou, Stanley Kruger","submitted_at":"2021-01-29T02:35:57Z","abstract_excerpt":"We introduce a novel generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The proposed generative framework represents the image time series as a smooth non-linear function of low-dimensional latent vectors that capture the cardiac and respiratory phases. The non-linear function is represented using a deep convolutional neural network (CNN). Unlike the popular CNN approaches that require extensive fully-sampled training data that is not available in this setting, the parameters of the CNN generator as well"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.12366","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/2101.12366/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":"2101.12366","created_at":"2026-07-05T02:10:43.119800+00:00"},{"alias_kind":"arxiv_version","alias_value":"2101.12366v1","created_at":"2026-07-05T02:10:43.119800+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.12366","created_at":"2026-07-05T02:10:43.119800+00:00"},{"alias_kind":"pith_short_12","alias_value":"GKIQR7ENFONW","created_at":"2026-07-05T02:10:43.119800+00:00"},{"alias_kind":"pith_short_16","alias_value":"GKIQR7ENFONWKLK3","created_at":"2026-07-05T02:10:43.119800+00:00"},{"alias_kind":"pith_short_8","alias_value":"GKIQR7EN","created_at":"2026-07-05T02:10:43.119800+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/GKIQR7ENFONWKLK3GDW4HBPLOK","json":"https://pith.science/pith/GKIQR7ENFONWKLK3GDW4HBPLOK.json","graph_json":"https://pith.science/api/pith-number/GKIQR7ENFONWKLK3GDW4HBPLOK/graph.json","events_json":"https://pith.science/api/pith-number/GKIQR7ENFONWKLK3GDW4HBPLOK/events.json","paper":"https://pith.science/paper/GKIQR7EN"},"agent_actions":{"view_html":"https://pith.science/pith/GKIQR7ENFONWKLK3GDW4HBPLOK","download_json":"https://pith.science/pith/GKIQR7ENFONWKLK3GDW4HBPLOK.json","view_paper":"https://pith.science/paper/GKIQR7EN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2101.12366&json=true","fetch_graph":"https://pith.science/api/pith-number/GKIQR7ENFONWKLK3GDW4HBPLOK/graph.json","fetch_events":"https://pith.science/api/pith-number/GKIQR7ENFONWKLK3GDW4HBPLOK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GKIQR7ENFONWKLK3GDW4HBPLOK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GKIQR7ENFONWKLK3GDW4HBPLOK/action/storage_attestation","attest_author":"https://pith.science/pith/GKIQR7ENFONWKLK3GDW4HBPLOK/action/author_attestation","sign_citation":"https://pith.science/pith/GKIQR7ENFONWKLK3GDW4HBPLOK/action/citation_signature","submit_replication":"https://pith.science/pith/GKIQR7ENFONWKLK3GDW4HBPLOK/action/replication_record"}},"created_at":"2026-07-05T02:10:43.119800+00:00","updated_at":"2026-07-05T02:10:43.119800+00:00"}