{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ZFDAZR5AZ62WSVZGHUH6PJSUBI","short_pith_number":"pith:ZFDAZR5A","schema_version":"1.0","canonical_sha256":"c9460cc7a0cfb56957263d0fe7a6540a20a8b73d70a0b15840a1b3688c1f2ae0","source":{"kind":"arxiv","id":"1712.08726","version":2},"attestation_state":"computed","paper":{"title":"Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dongsheng Jiang, Luc Vosters, Tao Tan, Weiqiang Dou, Xiayu Xu, Yue Sun","submitted_at":"2017-12-23T07:35:51Z","abstract_excerpt":"The denoising of magnetic resonance (MR) images is a task of great importance for improving the acquired image quality. Many methods have been proposed in the literature to retrieve noise free images with good performances. Howerever, the state-of-the-art denoising methods, all needs a time-consuming optimization processes and their performance strongly depend on the estimated noise level parameter. Within this manuscript we propose the idea of denoising MRI Rician noise using a convolutional neural network. The advantage of the proposed methodology is that the learning based model can be dire"},"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":"1712.08726","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-12-23T07:35:51Z","cross_cats_sorted":[],"title_canon_sha256":"0624756f85e2d88dda524ecd6eff53d9802d4ab2bc01eba765f907b964c9d8b0","abstract_canon_sha256":"76d7a212908b81babe9439918c28d2a1e6f85d5dcd7d5cd5b54aa53b7ed1bd87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:41.800281Z","signature_b64":"CxBRzuSBW8WUN4uFPx+rDuOWNwtGB5y2todYWUXhManFT0gD6Hq+sueSQeYauDjRzPjYlrzPRvNOLWj6mcIDBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9460cc7a0cfb56957263d0fe7a6540a20a8b73d70a0b15840a1b3688c1f2ae0","last_reissued_at":"2026-05-18T00:24:41.799592Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:41.799592Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Denoising of 3D magnetic resonance images with multi-channel residual learning of convolutional neural network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dongsheng Jiang, Luc Vosters, Tao Tan, Weiqiang Dou, Xiayu Xu, Yue Sun","submitted_at":"2017-12-23T07:35:51Z","abstract_excerpt":"The denoising of magnetic resonance (MR) images is a task of great importance for improving the acquired image quality. Many methods have been proposed in the literature to retrieve noise free images with good performances. Howerever, the state-of-the-art denoising methods, all needs a time-consuming optimization processes and their performance strongly depend on the estimated noise level parameter. Within this manuscript we propose the idea of denoising MRI Rician noise using a convolutional neural network. The advantage of the proposed methodology is that the learning based model can be dire"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.08726","kind":"arxiv","version":2},"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":"1712.08726","created_at":"2026-05-18T00:24:41.799709+00:00"},{"alias_kind":"arxiv_version","alias_value":"1712.08726v2","created_at":"2026-05-18T00:24:41.799709+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.08726","created_at":"2026-05-18T00:24:41.799709+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZFDAZR5AZ62W","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZFDAZR5AZ62WSVZG","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZFDAZR5A","created_at":"2026-05-18T12:31:59.375834+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/ZFDAZR5AZ62WSVZGHUH6PJSUBI","json":"https://pith.science/pith/ZFDAZR5AZ62WSVZGHUH6PJSUBI.json","graph_json":"https://pith.science/api/pith-number/ZFDAZR5AZ62WSVZGHUH6PJSUBI/graph.json","events_json":"https://pith.science/api/pith-number/ZFDAZR5AZ62WSVZGHUH6PJSUBI/events.json","paper":"https://pith.science/paper/ZFDAZR5A"},"agent_actions":{"view_html":"https://pith.science/pith/ZFDAZR5AZ62WSVZGHUH6PJSUBI","download_json":"https://pith.science/pith/ZFDAZR5AZ62WSVZGHUH6PJSUBI.json","view_paper":"https://pith.science/paper/ZFDAZR5A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1712.08726&json=true","fetch_graph":"https://pith.science/api/pith-number/ZFDAZR5AZ62WSVZGHUH6PJSUBI/graph.json","fetch_events":"https://pith.science/api/pith-number/ZFDAZR5AZ62WSVZGHUH6PJSUBI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZFDAZR5AZ62WSVZGHUH6PJSUBI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZFDAZR5AZ62WSVZGHUH6PJSUBI/action/storage_attestation","attest_author":"https://pith.science/pith/ZFDAZR5AZ62WSVZGHUH6PJSUBI/action/author_attestation","sign_citation":"https://pith.science/pith/ZFDAZR5AZ62WSVZGHUH6PJSUBI/action/citation_signature","submit_replication":"https://pith.science/pith/ZFDAZR5AZ62WSVZGHUH6PJSUBI/action/replication_record"}},"created_at":"2026-05-18T00:24:41.799709+00:00","updated_at":"2026-05-18T00:24:41.799709+00:00"}