{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:HGNAYS5TTWWGZ7JAU5B2C2R5CM","short_pith_number":"pith:HGNAYS5T","schema_version":"1.0","canonical_sha256":"399a0c4bb39dac6cfd20a743a16a3d13055c678c2ec63009da35c9ccf47e44c1","source":{"kind":"arxiv","id":"1710.06304","version":1},"attestation_state":"computed","paper":{"title":"Towards CT-quality Ultrasound Imaging using Deep Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["eess.IV","physics.med-ph"],"primary_cat":"cs.CV","authors_text":"Alex M. Bronstein, Michael Zibulevsky, Oleg V. Michailovich, Ortal Senouf, Sanketh Vedula","submitted_at":"2017-10-17T14:11:57Z","abstract_excerpt":"The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis. Unfortunately, the beam-forming based image formation produces granular speckle noise, blurring, shading and other artifacts. To overcome these effects, the ultimate goal would be to reconstruct the tissue acoustic properties by solving a full wave propagation inverse problem. In this work, we make a step towards this goal, using Multi-Resolution Convolutional Neural Networks (CNN). As a result, we are able to reconstruct CT-quality images from the reflec"},"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":"1710.06304","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2017-10-17T14:11:57Z","cross_cats_sorted":["eess.IV","physics.med-ph"],"title_canon_sha256":"8d355337b32e44dda08410b3e802a7596d0acf54922be1b7c3a4abaa1033baf1","abstract_canon_sha256":"be7db69c659a1875883a247c92832429a4d4191ddef84358873300b079a859a6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:32.296105Z","signature_b64":"wog654oDUESKsioVxB/fDIrgKUsRfgoj0gQVVKAMBIh+yxiqIpuwAkY7+1bzhzdIde3X2UFvC7IBYhmlACrRDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"399a0c4bb39dac6cfd20a743a16a3d13055c678c2ec63009da35c9ccf47e44c1","last_reissued_at":"2026-05-18T00:32:32.295394Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:32.295394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards CT-quality Ultrasound Imaging using Deep Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["eess.IV","physics.med-ph"],"primary_cat":"cs.CV","authors_text":"Alex M. Bronstein, Michael Zibulevsky, Oleg V. Michailovich, Ortal Senouf, Sanketh Vedula","submitted_at":"2017-10-17T14:11:57Z","abstract_excerpt":"The cost-effectiveness and practical harmlessness of ultrasound imaging have made it one of the most widespread tools for medical diagnosis. Unfortunately, the beam-forming based image formation produces granular speckle noise, blurring, shading and other artifacts. To overcome these effects, the ultimate goal would be to reconstruct the tissue acoustic properties by solving a full wave propagation inverse problem. In this work, we make a step towards this goal, using Multi-Resolution Convolutional Neural Networks (CNN). As a result, we are able to reconstruct CT-quality images from the reflec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.06304","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1710.06304","created_at":"2026-05-18T00:32:32.295512+00:00"},{"alias_kind":"arxiv_version","alias_value":"1710.06304v1","created_at":"2026-05-18T00:32:32.295512+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.06304","created_at":"2026-05-18T00:32:32.295512+00:00"},{"alias_kind":"pith_short_12","alias_value":"HGNAYS5TTWWG","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_16","alias_value":"HGNAYS5TTWWGZ7JA","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_8","alias_value":"HGNAYS5T","created_at":"2026-05-18T12:31:18.294218+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/HGNAYS5TTWWGZ7JAU5B2C2R5CM","json":"https://pith.science/pith/HGNAYS5TTWWGZ7JAU5B2C2R5CM.json","graph_json":"https://pith.science/api/pith-number/HGNAYS5TTWWGZ7JAU5B2C2R5CM/graph.json","events_json":"https://pith.science/api/pith-number/HGNAYS5TTWWGZ7JAU5B2C2R5CM/events.json","paper":"https://pith.science/paper/HGNAYS5T"},"agent_actions":{"view_html":"https://pith.science/pith/HGNAYS5TTWWGZ7JAU5B2C2R5CM","download_json":"https://pith.science/pith/HGNAYS5TTWWGZ7JAU5B2C2R5CM.json","view_paper":"https://pith.science/paper/HGNAYS5T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1710.06304&json=true","fetch_graph":"https://pith.science/api/pith-number/HGNAYS5TTWWGZ7JAU5B2C2R5CM/graph.json","fetch_events":"https://pith.science/api/pith-number/HGNAYS5TTWWGZ7JAU5B2C2R5CM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HGNAYS5TTWWGZ7JAU5B2C2R5CM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HGNAYS5TTWWGZ7JAU5B2C2R5CM/action/storage_attestation","attest_author":"https://pith.science/pith/HGNAYS5TTWWGZ7JAU5B2C2R5CM/action/author_attestation","sign_citation":"https://pith.science/pith/HGNAYS5TTWWGZ7JAU5B2C2R5CM/action/citation_signature","submit_replication":"https://pith.science/pith/HGNAYS5TTWWGZ7JAU5B2C2R5CM/action/replication_record"}},"created_at":"2026-05-18T00:32:32.295512+00:00","updated_at":"2026-05-18T00:32:32.295512+00:00"}