{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:VIIAVGOLHKOBVYOFXJQ76I26OB","short_pith_number":"pith:VIIAVGOL","schema_version":"1.0","canonical_sha256":"aa100a99cb3a9c1ae1c5ba61ff235e707384b04b8fc72583378c45484d0a6484","source":{"kind":"arxiv","id":"2108.00274","version":2},"attestation_state":"computed","paper":{"title":"Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Alejandro F Frangi, Dong Ni, Mingyuan Luo, Nishant Ravikumar, Xiaoqiong Huang, Xindi Hu, Xin Yang, Yuhao Huang, Yuxin Zou","submitted_at":"2021-07-31T16:06:50Z","abstract_excerpt":"3D ultrasound (US) is widely used for its rich diagnostic information. However, it is criticized for its limited field of view. 3D freehand US reconstruction is promising in addressing the problem by providing broad range and freeform scan. The existing deep learning based methods only focus on the basic cases of skill sequences, and the model relies on the training data heavily. The sequences in real clinical practice are a mix of diverse skills and have complex scanning paths. Besides, deep models should adapt themselves to the testing cases with prior knowledge for better robustness, rather"},"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":"2108.00274","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-07-31T16:06:50Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"4a674943bbe36f5e29a4734869e7d4a479c206857cb700bf488802b3d2a8fe97","abstract_canon_sha256":"c07f7b700d9c3ea4625679859ee85e69a91419ce7cb7e8d3c45c29b3a72148b4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:06:58.873992Z","signature_b64":"YxD+MiYJCSBrxIPEMf21xZ/x4Fihc+XP1KrhyHDjMq7p6LzRCBcl+9rPvWPKC2emPURhAlgqzO348wPrTDK+Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa100a99cb3a9c1ae1c5ba61ff235e707384b04b8fc72583378c45484d0a6484","last_reissued_at":"2026-07-05T03:06:58.873518Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:06:58.873518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Alejandro F Frangi, Dong Ni, Mingyuan Luo, Nishant Ravikumar, Xiaoqiong Huang, Xindi Hu, Xin Yang, Yuhao Huang, Yuxin Zou","submitted_at":"2021-07-31T16:06:50Z","abstract_excerpt":"3D ultrasound (US) is widely used for its rich diagnostic information. However, it is criticized for its limited field of view. 3D freehand US reconstruction is promising in addressing the problem by providing broad range and freeform scan. The existing deep learning based methods only focus on the basic cases of skill sequences, and the model relies on the training data heavily. The sequences in real clinical practice are a mix of diverse skills and have complex scanning paths. Besides, deep models should adapt themselves to the testing cases with prior knowledge for better robustness, rather"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.00274","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2108.00274/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":"2108.00274","created_at":"2026-07-05T03:06:58.873574+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.00274v2","created_at":"2026-07-05T03:06:58.873574+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.00274","created_at":"2026-07-05T03:06:58.873574+00:00"},{"alias_kind":"pith_short_12","alias_value":"VIIAVGOLHKOB","created_at":"2026-07-05T03:06:58.873574+00:00"},{"alias_kind":"pith_short_16","alias_value":"VIIAVGOLHKOBVYOF","created_at":"2026-07-05T03:06:58.873574+00:00"},{"alias_kind":"pith_short_8","alias_value":"VIIAVGOL","created_at":"2026-07-05T03:06:58.873574+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/VIIAVGOLHKOBVYOFXJQ76I26OB","json":"https://pith.science/pith/VIIAVGOLHKOBVYOFXJQ76I26OB.json","graph_json":"https://pith.science/api/pith-number/VIIAVGOLHKOBVYOFXJQ76I26OB/graph.json","events_json":"https://pith.science/api/pith-number/VIIAVGOLHKOBVYOFXJQ76I26OB/events.json","paper":"https://pith.science/paper/VIIAVGOL"},"agent_actions":{"view_html":"https://pith.science/pith/VIIAVGOLHKOBVYOFXJQ76I26OB","download_json":"https://pith.science/pith/VIIAVGOLHKOBVYOFXJQ76I26OB.json","view_paper":"https://pith.science/paper/VIIAVGOL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.00274&json=true","fetch_graph":"https://pith.science/api/pith-number/VIIAVGOLHKOBVYOFXJQ76I26OB/graph.json","fetch_events":"https://pith.science/api/pith-number/VIIAVGOLHKOBVYOFXJQ76I26OB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VIIAVGOLHKOBVYOFXJQ76I26OB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VIIAVGOLHKOBVYOFXJQ76I26OB/action/storage_attestation","attest_author":"https://pith.science/pith/VIIAVGOLHKOBVYOFXJQ76I26OB/action/author_attestation","sign_citation":"https://pith.science/pith/VIIAVGOLHKOBVYOFXJQ76I26OB/action/citation_signature","submit_replication":"https://pith.science/pith/VIIAVGOLHKOBVYOFXJQ76I26OB/action/replication_record"}},"created_at":"2026-07-05T03:06:58.873574+00:00","updated_at":"2026-07-05T03:06:58.873574+00:00"}