{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:MDVZSO32F3YNLFHPD2PDF6HUEV","short_pith_number":"pith:MDVZSO32","schema_version":"1.0","canonical_sha256":"60eb993b7a2ef0d594ef1e9e32f8f4256e4bd3452bbb770eb07b69eae04dcfd4","source":{"kind":"arxiv","id":"2402.17797","version":4},"attestation_state":"computed","paper":{"title":"Neural Radiance Fields in Medical Imaging: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Heng Fan, Hongtu Zhu, Shu Hu, Xin Li, Xin Wang, Yineng Chen","submitted_at":"2024-02-26T22:00:59Z","abstract_excerpt":"Neural Radiance Fields (NeRF), as a pioneering technique in computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data. However, they face unique challenges when applied to medical applications. This paper presents a comprehensive examination of applications of NeRFs in medical imaging, highlighting four imminent challenges, including fundamental imaging principles, inner structure requirement, object boundary definition, and color density significance. We discuss current methods on dif"},"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":"2402.17797","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2024-02-26T22:00:59Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"bf3dbd8c11262690ca59b4073398b1a262ba91d7261aa68671d59e4732c6c87c","abstract_canon_sha256":"449a95ffe449777d19ee5a9b522d92c92b1a436d603a34b34f587d815b2b7320"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:19:54.839161Z","signature_b64":"FiltXZ/EX5Itlicd7EFWAUYZNjaP2xl1cBiDQrjrgQ5uVOuW+Lnt1N9GRvGesl3im7ivtIKjZNRrRunpZ0YaBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60eb993b7a2ef0d594ef1e9e32f8f4256e4bd3452bbb770eb07b69eae04dcfd4","last_reissued_at":"2026-07-05T10:19:54.838677Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:19:54.838677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Radiance Fields in Medical Imaging: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Heng Fan, Hongtu Zhu, Shu Hu, Xin Li, Xin Wang, Yineng Chen","submitted_at":"2024-02-26T22:00:59Z","abstract_excerpt":"Neural Radiance Fields (NeRF), as a pioneering technique in computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data. However, they face unique challenges when applied to medical applications. This paper presents a comprehensive examination of applications of NeRFs in medical imaging, highlighting four imminent challenges, including fundamental imaging principles, inner structure requirement, object boundary definition, and color density significance. We discuss current methods on dif"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.17797","kind":"arxiv","version":4},"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/2402.17797/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":"2402.17797","created_at":"2026-07-05T10:19:54.838735+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.17797v4","created_at":"2026-07-05T10:19:54.838735+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.17797","created_at":"2026-07-05T10:19:54.838735+00:00"},{"alias_kind":"pith_short_12","alias_value":"MDVZSO32F3YN","created_at":"2026-07-05T10:19:54.838735+00:00"},{"alias_kind":"pith_short_16","alias_value":"MDVZSO32F3YNLFHP","created_at":"2026-07-05T10:19:54.838735+00:00"},{"alias_kind":"pith_short_8","alias_value":"MDVZSO32","created_at":"2026-07-05T10:19:54.838735+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.03320","citing_title":"Robust Multi-Source Covid-19 Detection in CT Images","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07674","citing_title":"Weight Group-wise Post-Training Quantization for Medical Foundation Model","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2604.06347","citing_title":"Evidence-Based Actor-Verifier Reasoning for Echocardiographic Agents","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MDVZSO32F3YNLFHPD2PDF6HUEV","json":"https://pith.science/pith/MDVZSO32F3YNLFHPD2PDF6HUEV.json","graph_json":"https://pith.science/api/pith-number/MDVZSO32F3YNLFHPD2PDF6HUEV/graph.json","events_json":"https://pith.science/api/pith-number/MDVZSO32F3YNLFHPD2PDF6HUEV/events.json","paper":"https://pith.science/paper/MDVZSO32"},"agent_actions":{"view_html":"https://pith.science/pith/MDVZSO32F3YNLFHPD2PDF6HUEV","download_json":"https://pith.science/pith/MDVZSO32F3YNLFHPD2PDF6HUEV.json","view_paper":"https://pith.science/paper/MDVZSO32","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.17797&json=true","fetch_graph":"https://pith.science/api/pith-number/MDVZSO32F3YNLFHPD2PDF6HUEV/graph.json","fetch_events":"https://pith.science/api/pith-number/MDVZSO32F3YNLFHPD2PDF6HUEV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MDVZSO32F3YNLFHPD2PDF6HUEV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MDVZSO32F3YNLFHPD2PDF6HUEV/action/storage_attestation","attest_author":"https://pith.science/pith/MDVZSO32F3YNLFHPD2PDF6HUEV/action/author_attestation","sign_citation":"https://pith.science/pith/MDVZSO32F3YNLFHPD2PDF6HUEV/action/citation_signature","submit_replication":"https://pith.science/pith/MDVZSO32F3YNLFHPD2PDF6HUEV/action/replication_record"}},"created_at":"2026-07-05T10:19:54.838735+00:00","updated_at":"2026-07-05T10:19:54.838735+00:00"}