{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JI2CXTPS5AZ6CB5ONJ4OKHSLCA","short_pith_number":"pith:JI2CXTPS","schema_version":"1.0","canonical_sha256":"4a342bcdf2e833e107ae6a78e51e4b1011bb015e3072b1a75e6189163cff41b2","source":{"kind":"arxiv","id":"2308.01173","version":2},"attestation_state":"computed","paper":{"title":"FlexDTI: Flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Congbo Cai, Dairong Cao, Jianfeng Bao, Jianzhong Lin, Jiechao Wang, Qinqin Yang, Shuhui Cai, Taishan Kang, Zejun Wu, Zhen Xing, Zhong Chen, Zunquan Chen","submitted_at":"2023-08-02T14:25:01Z","abstract_excerpt":"Objective: Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in the training data. This work aims to develop and evaluate a novel dynamic-convolution-based method called FlexDTI for highly efficient diffusion tensor reconstruction with flexible diffusion encoding gradient scheme. Approach: FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The method used dynamic convolution kernels to embed di"},"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":"2308.01173","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-08-02T14:25:01Z","cross_cats_sorted":[],"title_canon_sha256":"be2b43196a806ac3931d251d8e97cb48a2acc74fb85111a6d8eebdf60684f061","abstract_canon_sha256":"bed06432e502f08e862e13c38072f5e6688fea5e025c53ba9135b223ebde8a2c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:26:55.953459Z","signature_b64":"oYnptgN2ZTp9IvE1dp6Tiohe2HEuIdcgMuZj8zXQ7i7MwBqVpEF6b0t/58aaWmeu4cFmdS/Flse0Jg53i2ITDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a342bcdf2e833e107ae6a78e51e4b1011bb015e3072b1a75e6189163cff41b2","last_reissued_at":"2026-07-05T07:26:55.953016Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:26:55.953016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FlexDTI: Flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Congbo Cai, Dairong Cao, Jianfeng Bao, Jianzhong Lin, Jiechao Wang, Qinqin Yang, Shuhui Cai, Taishan Kang, Zejun Wu, Zhen Xing, Zhong Chen, Zunquan Chen","submitted_at":"2023-08-02T14:25:01Z","abstract_excerpt":"Objective: Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in the training data. This work aims to develop and evaluate a novel dynamic-convolution-based method called FlexDTI for highly efficient diffusion tensor reconstruction with flexible diffusion encoding gradient scheme. Approach: FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The method used dynamic convolution kernels to embed di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.01173","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/2308.01173/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":"2308.01173","created_at":"2026-07-05T07:26:55.953065+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.01173v2","created_at":"2026-07-05T07:26:55.953065+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.01173","created_at":"2026-07-05T07:26:55.953065+00:00"},{"alias_kind":"pith_short_12","alias_value":"JI2CXTPS5AZ6","created_at":"2026-07-05T07:26:55.953065+00:00"},{"alias_kind":"pith_short_16","alias_value":"JI2CXTPS5AZ6CB5O","created_at":"2026-07-05T07:26:55.953065+00:00"},{"alias_kind":"pith_short_8","alias_value":"JI2CXTPS","created_at":"2026-07-05T07:26:55.953065+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/JI2CXTPS5AZ6CB5ONJ4OKHSLCA","json":"https://pith.science/pith/JI2CXTPS5AZ6CB5ONJ4OKHSLCA.json","graph_json":"https://pith.science/api/pith-number/JI2CXTPS5AZ6CB5ONJ4OKHSLCA/graph.json","events_json":"https://pith.science/api/pith-number/JI2CXTPS5AZ6CB5ONJ4OKHSLCA/events.json","paper":"https://pith.science/paper/JI2CXTPS"},"agent_actions":{"view_html":"https://pith.science/pith/JI2CXTPS5AZ6CB5ONJ4OKHSLCA","download_json":"https://pith.science/pith/JI2CXTPS5AZ6CB5ONJ4OKHSLCA.json","view_paper":"https://pith.science/paper/JI2CXTPS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.01173&json=true","fetch_graph":"https://pith.science/api/pith-number/JI2CXTPS5AZ6CB5ONJ4OKHSLCA/graph.json","fetch_events":"https://pith.science/api/pith-number/JI2CXTPS5AZ6CB5ONJ4OKHSLCA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JI2CXTPS5AZ6CB5ONJ4OKHSLCA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JI2CXTPS5AZ6CB5ONJ4OKHSLCA/action/storage_attestation","attest_author":"https://pith.science/pith/JI2CXTPS5AZ6CB5ONJ4OKHSLCA/action/author_attestation","sign_citation":"https://pith.science/pith/JI2CXTPS5AZ6CB5ONJ4OKHSLCA/action/citation_signature","submit_replication":"https://pith.science/pith/JI2CXTPS5AZ6CB5ONJ4OKHSLCA/action/replication_record"}},"created_at":"2026-07-05T07:26:55.953065+00:00","updated_at":"2026-07-05T07:26:55.953065+00:00"}