{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UMONSWQ7NS25T3TNMTNRNWEPIL","short_pith_number":"pith:UMONSWQ7","schema_version":"1.0","canonical_sha256":"a31cd95a1f6cb5d9ee6d64db16d88f42e97143313a0693be044cb1c914bef027","source":{"kind":"arxiv","id":"2310.11641","version":1},"attestation_state":"computed","paper":{"title":"Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Di Guo, Jianyun Cai, Jing Li, Xiaobo Qu, Yanhuang Wu, Yirong Zhou, Yongfu You, Yuhan Su","submitted_at":"2023-10-18T00:35:05Z","abstract_excerpt":"Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud comput"},"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":"2310.11641","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-10-18T00:35:05Z","cross_cats_sorted":["cs.AI","physics.med-ph"],"title_canon_sha256":"7dad7fa3d45d79f84d945e052745828867cb64ad144bbf5ad6c359102c1a0779","abstract_canon_sha256":"4f0bbe71620c79ad12c2ac53c635ec74c7c77878f60fa06aedd3a565c91800d3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:02:10.406091Z","signature_b64":"z+VeNBOfXj3oaDHayWhox1ktWMxZLNiwzFNho94KZNHLy1ZBo9uoo5t7yAd+pjkC00jTvYSHC/6xvpi3LYKEBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a31cd95a1f6cb5d9ee6d64db16d88f42e97143313a0693be044cb1c914bef027","last_reissued_at":"2026-07-05T07:02:10.405700Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:02:10.405700Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cloud-Magnetic Resonance Imaging System: In the Era of 6G and Artificial Intelligence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Di Guo, Jianyun Cai, Jing Li, Xiaobo Qu, Yanhuang Wu, Yirong Zhou, Yongfu You, Yuhan Su","submitted_at":"2023-10-18T00:35:05Z","abstract_excerpt":"Magnetic Resonance Imaging (MRI) plays an important role in medical diagnosis, generating petabytes of image data annually in large hospitals. This voluminous data stream requires a significant amount of network bandwidth and extensive storage infrastructure. Additionally, local data processing demands substantial manpower and hardware investments. Data isolation across different healthcare institutions hinders cross-institutional collaboration in clinics and research. In this work, we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud comput"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.11641","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/2310.11641/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":"2310.11641","created_at":"2026-07-05T07:02:10.405757+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.11641v1","created_at":"2026-07-05T07:02:10.405757+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.11641","created_at":"2026-07-05T07:02:10.405757+00:00"},{"alias_kind":"pith_short_12","alias_value":"UMONSWQ7NS25","created_at":"2026-07-05T07:02:10.405757+00:00"},{"alias_kind":"pith_short_16","alias_value":"UMONSWQ7NS25T3TN","created_at":"2026-07-05T07:02:10.405757+00:00"},{"alias_kind":"pith_short_8","alias_value":"UMONSWQ7","created_at":"2026-07-05T07:02:10.405757+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/UMONSWQ7NS25T3TNMTNRNWEPIL","json":"https://pith.science/pith/UMONSWQ7NS25T3TNMTNRNWEPIL.json","graph_json":"https://pith.science/api/pith-number/UMONSWQ7NS25T3TNMTNRNWEPIL/graph.json","events_json":"https://pith.science/api/pith-number/UMONSWQ7NS25T3TNMTNRNWEPIL/events.json","paper":"https://pith.science/paper/UMONSWQ7"},"agent_actions":{"view_html":"https://pith.science/pith/UMONSWQ7NS25T3TNMTNRNWEPIL","download_json":"https://pith.science/pith/UMONSWQ7NS25T3TNMTNRNWEPIL.json","view_paper":"https://pith.science/paper/UMONSWQ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.11641&json=true","fetch_graph":"https://pith.science/api/pith-number/UMONSWQ7NS25T3TNMTNRNWEPIL/graph.json","fetch_events":"https://pith.science/api/pith-number/UMONSWQ7NS25T3TNMTNRNWEPIL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UMONSWQ7NS25T3TNMTNRNWEPIL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UMONSWQ7NS25T3TNMTNRNWEPIL/action/storage_attestation","attest_author":"https://pith.science/pith/UMONSWQ7NS25T3TNMTNRNWEPIL/action/author_attestation","sign_citation":"https://pith.science/pith/UMONSWQ7NS25T3TNMTNRNWEPIL/action/citation_signature","submit_replication":"https://pith.science/pith/UMONSWQ7NS25T3TNMTNRNWEPIL/action/replication_record"}},"created_at":"2026-07-05T07:02:10.405757+00:00","updated_at":"2026-07-05T07:02:10.405757+00:00"}