{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:EL7DYGPV2JCJUJS6HAXKVYLADH","short_pith_number":"pith:EL7DYGPV","schema_version":"1.0","canonical_sha256":"22fe3c19f5d2449a265e382eaae16019ded8af7aab1adb706b70d4919df869c5","source":{"kind":"arxiv","id":"1609.08508","version":1},"attestation_state":"computed","paper":{"title":"Low-Dose CT via Deep Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.med-ph","authors_text":"Ge Wang, Hu Chen, Jiliu Zhou, Ke Li, Peixi Liao, Weihua Zhang, Yi Zhang","submitted_at":"2016-09-27T15:57:30Z","abstract_excerpt":"In order to reduce the potential radiation risk, low-dose CT has attracted more and more attention. However, simply lowering the radiation dose will significantly degrade the imaging quality. In this paper, we propose a noise reduction method for low-dose CT via deep learning without accessing the original projection data. An architecture of deep convolutional neural network was considered to map the low-dose CT images into its corresponding normal-dose CT images patch by patch. Qualitative and quantitative evaluations demonstrate a state-the-art performance of the proposed method."},"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":"1609.08508","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.med-ph","submitted_at":"2016-09-27T15:57:30Z","cross_cats_sorted":[],"title_canon_sha256":"0ae9a9cc66d6f47a48a46428e84092b8dc9dec678d6afe62685b11125142f5e8","abstract_canon_sha256":"4e32ef4b7b3e479f11e8f780b85c31c899933b19bd773fe48d7dfb9163ee46ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:45.751984Z","signature_b64":"BJjyAbjkisEPAEMGITxXmXvhxSPLlJ6THH1JERLWIhuu0Mb9UTLrQF4yS37azXIysoZkXcwk9VsfgYuVVxVjDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22fe3c19f5d2449a265e382eaae16019ded8af7aab1adb706b70d4919df869c5","last_reissued_at":"2026-05-18T01:03:45.751553Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:45.751553Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Low-Dose CT via Deep Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.med-ph","authors_text":"Ge Wang, Hu Chen, Jiliu Zhou, Ke Li, Peixi Liao, Weihua Zhang, Yi Zhang","submitted_at":"2016-09-27T15:57:30Z","abstract_excerpt":"In order to reduce the potential radiation risk, low-dose CT has attracted more and more attention. However, simply lowering the radiation dose will significantly degrade the imaging quality. In this paper, we propose a noise reduction method for low-dose CT via deep learning without accessing the original projection data. An architecture of deep convolutional neural network was considered to map the low-dose CT images into its corresponding normal-dose CT images patch by patch. Qualitative and quantitative evaluations demonstrate a state-the-art performance of the proposed method."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.08508","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":"1609.08508","created_at":"2026-05-18T01:03:45.751617+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.08508v1","created_at":"2026-05-18T01:03:45.751617+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.08508","created_at":"2026-05-18T01:03:45.751617+00:00"},{"alias_kind":"pith_short_12","alias_value":"EL7DYGPV2JCJ","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_16","alias_value":"EL7DYGPV2JCJUJS6","created_at":"2026-05-18T12:30:12.583610+00:00"},{"alias_kind":"pith_short_8","alias_value":"EL7DYGPV","created_at":"2026-05-18T12:30:12.583610+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/EL7DYGPV2JCJUJS6HAXKVYLADH","json":"https://pith.science/pith/EL7DYGPV2JCJUJS6HAXKVYLADH.json","graph_json":"https://pith.science/api/pith-number/EL7DYGPV2JCJUJS6HAXKVYLADH/graph.json","events_json":"https://pith.science/api/pith-number/EL7DYGPV2JCJUJS6HAXKVYLADH/events.json","paper":"https://pith.science/paper/EL7DYGPV"},"agent_actions":{"view_html":"https://pith.science/pith/EL7DYGPV2JCJUJS6HAXKVYLADH","download_json":"https://pith.science/pith/EL7DYGPV2JCJUJS6HAXKVYLADH.json","view_paper":"https://pith.science/paper/EL7DYGPV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.08508&json=true","fetch_graph":"https://pith.science/api/pith-number/EL7DYGPV2JCJUJS6HAXKVYLADH/graph.json","fetch_events":"https://pith.science/api/pith-number/EL7DYGPV2JCJUJS6HAXKVYLADH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EL7DYGPV2JCJUJS6HAXKVYLADH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EL7DYGPV2JCJUJS6HAXKVYLADH/action/storage_attestation","attest_author":"https://pith.science/pith/EL7DYGPV2JCJUJS6HAXKVYLADH/action/author_attestation","sign_citation":"https://pith.science/pith/EL7DYGPV2JCJUJS6HAXKVYLADH/action/citation_signature","submit_replication":"https://pith.science/pith/EL7DYGPV2JCJUJS6HAXKVYLADH/action/replication_record"}},"created_at":"2026-05-18T01:03:45.751617+00:00","updated_at":"2026-05-18T01:03:45.751617+00:00"}