{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:BMZP3WANLNUKFLZJTYMXMV72AS","short_pith_number":"pith:BMZP3WAN","schema_version":"1.0","canonical_sha256":"0b32fdd80d5b68a2af299e197657fa049b640d4e51e3d922a3574c2bbecae211","source":{"kind":"arxiv","id":"1903.09272","version":1},"attestation_state":"computed","paper":{"title":"Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Qinmu Peng, Shi Yin, Xinge You, Zhengqiang Zhang","submitted_at":"2019-03-21T23:56:16Z","abstract_excerpt":"High angular resolution diffusion imaging (HARDI) demands a lager amount of data measurements compared to diffusion tensor imaging, restricting its use in practice. In this work, we explore a learning-based approach to reconstruct HARDI from a smaller number of measurements in q-space. The approach aims to directly learn the mapping relationship between the measured and HARDI signals from the collecting HARDI acquisitions of other subjects. Specifically, the mapping is represented as a 1D encoder-decoder convolutional neural network under the guidance of the compressed sensing (CS) theory for "},"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":"1903.09272","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-21T23:56:16Z","cross_cats_sorted":[],"title_canon_sha256":"5e51789bd659a787310f9a436d811b7c5bb2fe9a1410ecdb5279cd705df202ba","abstract_canon_sha256":"47f900c8f94cee74f40ad920e490b9f4504baa85230577cf8b73061929a8bb3e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:50:40.151431Z","signature_b64":"kiHnyQJM+UFYomuZ3GP8mOGoMGg+7jGr78p4iv1TsraB5gJY9q1A8NjxaynMgXRkE+UHLGeL60LS5ty6Do/JBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b32fdd80d5b68a2af299e197657fa049b640d4e51e3d922a3574c2bbecae211","last_reissued_at":"2026-05-17T23:50:40.150742Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:50:40.150742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast and accurate reconstruction of HARDI using a 1D encoder-decoder convolutional network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Qinmu Peng, Shi Yin, Xinge You, Zhengqiang Zhang","submitted_at":"2019-03-21T23:56:16Z","abstract_excerpt":"High angular resolution diffusion imaging (HARDI) demands a lager amount of data measurements compared to diffusion tensor imaging, restricting its use in practice. In this work, we explore a learning-based approach to reconstruct HARDI from a smaller number of measurements in q-space. The approach aims to directly learn the mapping relationship between the measured and HARDI signals from the collecting HARDI acquisitions of other subjects. Specifically, the mapping is represented as a 1D encoder-decoder convolutional neural network under the guidance of the compressed sensing (CS) theory for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.09272","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":"1903.09272","created_at":"2026-05-17T23:50:40.150839+00:00"},{"alias_kind":"arxiv_version","alias_value":"1903.09272v1","created_at":"2026-05-17T23:50:40.150839+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.09272","created_at":"2026-05-17T23:50:40.150839+00:00"},{"alias_kind":"pith_short_12","alias_value":"BMZP3WANLNUK","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"BMZP3WANLNUKFLZJ","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"BMZP3WAN","created_at":"2026-05-18T12:33:12.712433+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/BMZP3WANLNUKFLZJTYMXMV72AS","json":"https://pith.science/pith/BMZP3WANLNUKFLZJTYMXMV72AS.json","graph_json":"https://pith.science/api/pith-number/BMZP3WANLNUKFLZJTYMXMV72AS/graph.json","events_json":"https://pith.science/api/pith-number/BMZP3WANLNUKFLZJTYMXMV72AS/events.json","paper":"https://pith.science/paper/BMZP3WAN"},"agent_actions":{"view_html":"https://pith.science/pith/BMZP3WANLNUKFLZJTYMXMV72AS","download_json":"https://pith.science/pith/BMZP3WANLNUKFLZJTYMXMV72AS.json","view_paper":"https://pith.science/paper/BMZP3WAN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1903.09272&json=true","fetch_graph":"https://pith.science/api/pith-number/BMZP3WANLNUKFLZJTYMXMV72AS/graph.json","fetch_events":"https://pith.science/api/pith-number/BMZP3WANLNUKFLZJTYMXMV72AS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BMZP3WANLNUKFLZJTYMXMV72AS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BMZP3WANLNUKFLZJTYMXMV72AS/action/storage_attestation","attest_author":"https://pith.science/pith/BMZP3WANLNUKFLZJTYMXMV72AS/action/author_attestation","sign_citation":"https://pith.science/pith/BMZP3WANLNUKFLZJTYMXMV72AS/action/citation_signature","submit_replication":"https://pith.science/pith/BMZP3WANLNUKFLZJTYMXMV72AS/action/replication_record"}},"created_at":"2026-05-17T23:50:40.150839+00:00","updated_at":"2026-05-17T23:50:40.150839+00:00"}