{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ZTFYZX4C6XCR2XLPV3G7YACMUY","short_pith_number":"pith:ZTFYZX4C","schema_version":"1.0","canonical_sha256":"cccb8cdf82f5c51d5d6faecdfc004ca639591c4d399cad2e46fb041fb34baaec","source":{"kind":"arxiv","id":"2606.00156","version":1},"attestation_state":"computed","paper":{"title":"A physics-informed foundation model for quantitative diffusion MRI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"eess.IV","authors_text":"Congyu Liao, Fang Chen, Haibo Qu, Hongen Liao, Hongjia Yang, Huaiqiang Sun, Jialan Zheng, Kasidit Anmahapong, Mingxuan Liu, Qiyuan Tian, Rui Li, Xue Zhang, Xun Yuan, Yang Yang, Yifei Chen, Yi Liao, Yuhang He, Zhuhao Wang, Ziang Wang, Zihan Li, Ziyu Li","submitted_at":"2026-05-29T07:26:35Z","abstract_excerpt":"Understanding the human brain requires access to its microscopic tissue architecture. Diffusion magnetic resonance imaging (MRI) provides the only noninvasive window into whole-brain microstructure in vivo, yet reliable quantitative mapping remains confined to specialized research settings requiring dense sampling and optimized acquisition protocols. To address this gap, we present a physics-informed generative microstructure network (PIGMENT) that learns a universal generative prior of human brain microstructure and adapts it zero-shot to each participant's measured data to recover subject-sp"},"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":"2606.00156","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2026-05-29T07:26:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"8962d65911209e97e9aa5c41b97796832f905fd57a9c8539310464cb8040aa53","abstract_canon_sha256":"9aa9b49104b85ee3ff4aee666ba7fe0ae2d5a62b9b9356902b5a9db8432a8a7f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T01:03:20.070605Z","signature_b64":"0p2br9555WvtHHh/ATLXlAmWdJgrxaSjP1/zAiT158IxLf0PBUYV2oDWV+LBv/PrE+QkdyHTtdJYX+olVCY4Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cccb8cdf82f5c51d5d6faecdfc004ca639591c4d399cad2e46fb041fb34baaec","last_reissued_at":"2026-06-02T01:03:20.070249Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T01:03:20.070249Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A physics-informed foundation model for quantitative diffusion MRI","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"eess.IV","authors_text":"Congyu Liao, Fang Chen, Haibo Qu, Hongen Liao, Hongjia Yang, Huaiqiang Sun, Jialan Zheng, Kasidit Anmahapong, Mingxuan Liu, Qiyuan Tian, Rui Li, Xue Zhang, Xun Yuan, Yang Yang, Yifei Chen, Yi Liao, Yuhang He, Zhuhao Wang, Ziang Wang, Zihan Li, Ziyu Li","submitted_at":"2026-05-29T07:26:35Z","abstract_excerpt":"Understanding the human brain requires access to its microscopic tissue architecture. Diffusion magnetic resonance imaging (MRI) provides the only noninvasive window into whole-brain microstructure in vivo, yet reliable quantitative mapping remains confined to specialized research settings requiring dense sampling and optimized acquisition protocols. To address this gap, we present a physics-informed generative microstructure network (PIGMENT) that learns a universal generative prior of human brain microstructure and adapts it zero-shot to each participant's measured data to recover subject-sp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00156","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.00156/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":"2606.00156","created_at":"2026-06-02T01:03:20.070304+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.00156v1","created_at":"2026-06-02T01:03:20.070304+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00156","created_at":"2026-06-02T01:03:20.070304+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZTFYZX4C6XCR","created_at":"2026-06-02T01:03:20.070304+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZTFYZX4C6XCR2XLP","created_at":"2026-06-02T01:03:20.070304+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZTFYZX4C","created_at":"2026-06-02T01:03:20.070304+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/ZTFYZX4C6XCR2XLPV3G7YACMUY","json":"https://pith.science/pith/ZTFYZX4C6XCR2XLPV3G7YACMUY.json","graph_json":"https://pith.science/api/pith-number/ZTFYZX4C6XCR2XLPV3G7YACMUY/graph.json","events_json":"https://pith.science/api/pith-number/ZTFYZX4C6XCR2XLPV3G7YACMUY/events.json","paper":"https://pith.science/paper/ZTFYZX4C"},"agent_actions":{"view_html":"https://pith.science/pith/ZTFYZX4C6XCR2XLPV3G7YACMUY","download_json":"https://pith.science/pith/ZTFYZX4C6XCR2XLPV3G7YACMUY.json","view_paper":"https://pith.science/paper/ZTFYZX4C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.00156&json=true","fetch_graph":"https://pith.science/api/pith-number/ZTFYZX4C6XCR2XLPV3G7YACMUY/graph.json","fetch_events":"https://pith.science/api/pith-number/ZTFYZX4C6XCR2XLPV3G7YACMUY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZTFYZX4C6XCR2XLPV3G7YACMUY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZTFYZX4C6XCR2XLPV3G7YACMUY/action/storage_attestation","attest_author":"https://pith.science/pith/ZTFYZX4C6XCR2XLPV3G7YACMUY/action/author_attestation","sign_citation":"https://pith.science/pith/ZTFYZX4C6XCR2XLPV3G7YACMUY/action/citation_signature","submit_replication":"https://pith.science/pith/ZTFYZX4C6XCR2XLPV3G7YACMUY/action/replication_record"}},"created_at":"2026-06-02T01:03:20.070304+00:00","updated_at":"2026-06-02T01:03:20.070304+00:00"}