{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:CT27PJ5GTF2XFEIXCTL34EGBBG","short_pith_number":"pith:CT27PJ5G","canonical_record":{"source":{"id":"1710.10006","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-27T06:49:37Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"b1b5995796122ca556fcb079c5eca9b324fbd0597629a78bc141d944b71a2b67","abstract_canon_sha256":"38798b2c6186cef843868094b6096adf2174ded6cc93768df78726ef57e75adc"},"schema_version":"1.0"},"canonical_sha256":"14f5f7a7a6997572911714d7be10c109bbabb12263f7e17b4a196153b65de53e","source":{"kind":"arxiv","id":"1710.10006","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.10006","created_at":"2026-05-18T00:31:50Z"},{"alias_kind":"arxiv_version","alias_value":"1710.10006v1","created_at":"2026-05-18T00:31:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10006","created_at":"2026-05-18T00:31:50Z"},{"alias_kind":"pith_short_12","alias_value":"CT27PJ5GTF2X","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CT27PJ5GTF2XFEIX","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CT27PJ5G","created_at":"2026-05-18T12:31:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:CT27PJ5GTF2XFEIXCTL34EGBBG","target":"record","payload":{"canonical_record":{"source":{"id":"1710.10006","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-27T06:49:37Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"b1b5995796122ca556fcb079c5eca9b324fbd0597629a78bc141d944b71a2b67","abstract_canon_sha256":"38798b2c6186cef843868094b6096adf2174ded6cc93768df78726ef57e75adc"},"schema_version":"1.0"},"canonical_sha256":"14f5f7a7a6997572911714d7be10c109bbabb12263f7e17b4a196153b65de53e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:50.109866Z","signature_b64":"nZKuHdscYGSnS+Ox2hm6luAMDNXpPKzvWzCHEHNDZlZPgtlkRiSACdeKXwLRa7Yw9doLIyKWF7kS39oXNmK7Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14f5f7a7a6997572911714d7be10c109bbabb12263f7e17b4a196153b65de53e","last_reissued_at":"2026-05-18T00:31:50.109235Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:50.109235Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.10006","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:31:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZTslgRjYwpih2XnmX8HuItRV6eZeHiNADf3yBeL3JFSR7riYBO42A4KGmOIIQCQ8Q2jZQ9w1Zn+hMMFPSuseAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T19:12:57.781076Z"},"content_sha256":"f57369a6b911bb2cc054243e45b474d739d7498a7dea6615eec321052aa95b8f","schema_version":"1.0","event_id":"sha256:f57369a6b911bb2cc054243e45b474d739d7498a7dea6615eec321052aa95b8f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:CT27PJ5GTF2XFEIXCTL34EGBBG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Learning for Accelerated Ultrasound Imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Jong Chul Ye, Yeo Hun Yoon","submitted_at":"2017-10-27T06:49:37Z","abstract_excerpt":"In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an increasing demand to reconstruct high quality images from limited number of data. However, the existing solutions require either hardware changes or computationally expansive algorithms. To overcome these limitations, here we propose a novel deep learning approach that interpolates the missing RF data by utilizing the sparsity of the RF data in the Fourier domain. Extensive experimental results from sub-sampled RF data from a real US system confirmed that the proposed method can effectively reduce the data rate withou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10006","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:31:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qerBYFBY6EOwVuA4+gL7URJsKg/uJ9dB7kmimnaLhY8DIirXwh+rlW5OuPIrGydzfR6yUDVHEBr0om6eP63qDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T19:12:57.781441Z"},"content_sha256":"bf3fb864a7574f9eaaf44c4f53e2d26564e905c5286c3448a7e46570402d6a9e","schema_version":"1.0","event_id":"sha256:bf3fb864a7574f9eaaf44c4f53e2d26564e905c5286c3448a7e46570402d6a9e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CT27PJ5GTF2XFEIXCTL34EGBBG/bundle.json","state_url":"https://pith.science/pith/CT27PJ5GTF2XFEIXCTL34EGBBG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CT27PJ5GTF2XFEIXCTL34EGBBG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-06T19:12:57Z","links":{"resolver":"https://pith.science/pith/CT27PJ5GTF2XFEIXCTL34EGBBG","bundle":"https://pith.science/pith/CT27PJ5GTF2XFEIXCTL34EGBBG/bundle.json","state":"https://pith.science/pith/CT27PJ5GTF2XFEIXCTL34EGBBG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CT27PJ5GTF2XFEIXCTL34EGBBG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:CT27PJ5GTF2XFEIXCTL34EGBBG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"38798b2c6186cef843868094b6096adf2174ded6cc93768df78726ef57e75adc","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-27T06:49:37Z","title_canon_sha256":"b1b5995796122ca556fcb079c5eca9b324fbd0597629a78bc141d944b71a2b67"},"schema_version":"1.0","source":{"id":"1710.10006","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.10006","created_at":"2026-05-18T00:31:50Z"},{"alias_kind":"arxiv_version","alias_value":"1710.10006v1","created_at":"2026-05-18T00:31:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.10006","created_at":"2026-05-18T00:31:50Z"},{"alias_kind":"pith_short_12","alias_value":"CT27PJ5GTF2X","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CT27PJ5GTF2XFEIX","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CT27PJ5G","created_at":"2026-05-18T12:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:bf3fb864a7574f9eaaf44c4f53e2d26564e905c5286c3448a7e46570402d6a9e","target":"graph","created_at":"2026-05-18T00:31:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an increasing demand to reconstruct high quality images from limited number of data. However, the existing solutions require either hardware changes or computationally expansive algorithms. To overcome these limitations, here we propose a novel deep learning approach that interpolates the missing RF data by utilizing the sparsity of the RF data in the Fourier domain. Extensive experimental results from sub-sampled RF data from a real US system confirmed that the proposed method can effectively reduce the data rate withou","authors_text":"Jong Chul Ye, Yeo Hun Yoon","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-27T06:49:37Z","title":"Deep Learning for Accelerated Ultrasound Imaging"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.10006","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f57369a6b911bb2cc054243e45b474d739d7498a7dea6615eec321052aa95b8f","target":"record","created_at":"2026-05-18T00:31:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"38798b2c6186cef843868094b6096adf2174ded6cc93768df78726ef57e75adc","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-10-27T06:49:37Z","title_canon_sha256":"b1b5995796122ca556fcb079c5eca9b324fbd0597629a78bc141d944b71a2b67"},"schema_version":"1.0","source":{"id":"1710.10006","kind":"arxiv","version":1}},"canonical_sha256":"14f5f7a7a6997572911714d7be10c109bbabb12263f7e17b4a196153b65de53e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"14f5f7a7a6997572911714d7be10c109bbabb12263f7e17b4a196153b65de53e","first_computed_at":"2026-05-18T00:31:50.109235Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:50.109235Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"nZKuHdscYGSnS+Ox2hm6luAMDNXpPKzvWzCHEHNDZlZPgtlkRiSACdeKXwLRa7Yw9doLIyKWF7kS39oXNmK7Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:50.109866Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.10006","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f57369a6b911bb2cc054243e45b474d739d7498a7dea6615eec321052aa95b8f","sha256:bf3fb864a7574f9eaaf44c4f53e2d26564e905c5286c3448a7e46570402d6a9e"],"state_sha256":"aeebd5b6b9267e17e989f42b2c1c7203531361ca73ef0f8d1adde391382e0385"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nzQ3RqcG3VMav+r7Wdqa26yAYTSJUdgAX4H7Qztpi3ApMOmkW0+/e4YxIoPvURmPhWNLFV+jQMxvha6NwTDbBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T19:12:57.783607Z","bundle_sha256":"fc7fc396cd60ed2b23e6e996c3368c7b182198adacdddf998bf608939676a46c"}}