{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:CW77MC2OZB7F74BFQIL67T6HL2","short_pith_number":"pith:CW77MC2O","schema_version":"1.0","canonical_sha256":"15bff60b4ec87e5ff0258217efcfc75eb79c4a64e4bbee11b1f7d125275a4dd0","source":{"kind":"arxiv","id":"1906.00650","version":1},"attestation_state":"computed","paper":{"title":"Deep Neural Network Assisted Iterative Reconstruction Method for Low Dose CT","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Jan De Beenhouwer, Jan Sijbers, Joris Soons, Shabab Bazrafkan, Vincent Van Nieuwenhove","submitted_at":"2019-06-03T09:09:54Z","abstract_excerpt":"Low Dose Computed Tomography suffers from a high amount of noise and/or undersampling artefacts in the reconstructed image. In the current article, a Deep Learning technique is exploited as a regularization term for the iterative reconstruction method SIRT. While SIRT minimizes the error in the sinogram space, the proposed regularization model additionally steers intermediate SIRT reconstructions towards the desired output. Extensive evaluations demonstrate the superior outcomes of the proposed method compared to the state of the art techniques. Comparing the forward projection of the reconstr"},"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":"1906.00650","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-06-03T09:09:54Z","cross_cats_sorted":[],"title_canon_sha256":"b3596a29f6346d17ef31b765e0abd320b0538e3ec6247a4b9453c7259abaeb85","abstract_canon_sha256":"aea579d93219f35d19e2e10e400c89f0dfa78fbc929d96e4bfa0dcab09488264"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:24.237265Z","signature_b64":"2az54yYnFGOcXk4d0LNUH7I72N8wVHQbFwHKF6Jnlj8bliw9RgP9XeEIcWMHYX3BaOv4aoJVfA3QdCB3geNkDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15bff60b4ec87e5ff0258217efcfc75eb79c4a64e4bbee11b1f7d125275a4dd0","last_reissued_at":"2026-05-17T23:44:24.236766Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:24.236766Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Neural Network Assisted Iterative Reconstruction Method for Low Dose CT","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Jan De Beenhouwer, Jan Sijbers, Joris Soons, Shabab Bazrafkan, Vincent Van Nieuwenhove","submitted_at":"2019-06-03T09:09:54Z","abstract_excerpt":"Low Dose Computed Tomography suffers from a high amount of noise and/or undersampling artefacts in the reconstructed image. In the current article, a Deep Learning technique is exploited as a regularization term for the iterative reconstruction method SIRT. While SIRT minimizes the error in the sinogram space, the proposed regularization model additionally steers intermediate SIRT reconstructions towards the desired output. Extensive evaluations demonstrate the superior outcomes of the proposed method compared to the state of the art techniques. Comparing the forward projection of the reconstr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.00650","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":"1906.00650","created_at":"2026-05-17T23:44:24.236840+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.00650v1","created_at":"2026-05-17T23:44:24.236840+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.00650","created_at":"2026-05-17T23:44:24.236840+00:00"},{"alias_kind":"pith_short_12","alias_value":"CW77MC2OZB7F","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"CW77MC2OZB7F74BF","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"CW77MC2O","created_at":"2026-05-18T12:33:15.570797+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/CW77MC2OZB7F74BFQIL67T6HL2","json":"https://pith.science/pith/CW77MC2OZB7F74BFQIL67T6HL2.json","graph_json":"https://pith.science/api/pith-number/CW77MC2OZB7F74BFQIL67T6HL2/graph.json","events_json":"https://pith.science/api/pith-number/CW77MC2OZB7F74BFQIL67T6HL2/events.json","paper":"https://pith.science/paper/CW77MC2O"},"agent_actions":{"view_html":"https://pith.science/pith/CW77MC2OZB7F74BFQIL67T6HL2","download_json":"https://pith.science/pith/CW77MC2OZB7F74BFQIL67T6HL2.json","view_paper":"https://pith.science/paper/CW77MC2O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.00650&json=true","fetch_graph":"https://pith.science/api/pith-number/CW77MC2OZB7F74BFQIL67T6HL2/graph.json","fetch_events":"https://pith.science/api/pith-number/CW77MC2OZB7F74BFQIL67T6HL2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CW77MC2OZB7F74BFQIL67T6HL2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CW77MC2OZB7F74BFQIL67T6HL2/action/storage_attestation","attest_author":"https://pith.science/pith/CW77MC2OZB7F74BFQIL67T6HL2/action/author_attestation","sign_citation":"https://pith.science/pith/CW77MC2OZB7F74BFQIL67T6HL2/action/citation_signature","submit_replication":"https://pith.science/pith/CW77MC2OZB7F74BFQIL67T6HL2/action/replication_record"}},"created_at":"2026-05-17T23:44:24.236840+00:00","updated_at":"2026-05-17T23:44:24.236840+00:00"}