{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QG66LTT2KWKXEQMDV4OD2YENUT","short_pith_number":"pith:QG66LTT2","schema_version":"1.0","canonical_sha256":"81bde5ce7a5595724183af1c3d608da4f03e227d88428b4b9e644a71a5039a15","source":{"kind":"arxiv","id":"2606.00146","version":1},"attestation_state":"computed","paper":{"title":"Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Dinggang Shen, Feng Li, Honglin Xiong, Lei Xiang, Qian Wang, Yulin Wang, Yuxian Tang","submitted_at":"2026-05-29T02:48:23Z","abstract_excerpt":"Motion artifacts in magnetic resonance imaging (MRI) degrade diagnostic reliability. Existing deep learning methods are typically contrast-specific and fail to generalize across diverse modalities and artifact severities. We propose a unified framework combining parameter-informed contrast disentanglement with severity-aware adaptive correction. ScanCLIP, pretrained on over 30,000 MRI text-image pairs, derives contrast embeddings from acquisition parameters to disentangle contrast style from anatomical content, yielding contrast-free features. A Vision Transformer then estimates motion severit"},"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":"2606.00146","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2026-05-29T02:48:23Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"af61db6e2add9e692ebd3826876c3d49fced68363d11160f348c7921cd9cab4c","abstract_canon_sha256":"6ab7100e5bb3862e1b0ff719692f39b2a6ff8b1250f1851baf9602ec586c971d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T01:03:19.741352Z","signature_b64":"0VWYuFWtxVnQnD1ZoHuG81KS4TJPF97ZYKTzqB2zpHjWj/n6o9qerRfIXgYWagTGc0KV78GBe6UjE8LFfTU4Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81bde5ce7a5595724183af1c3d608da4f03e227d88428b4b9e644a71a5039a15","last_reissued_at":"2026-06-02T01:03:19.740962Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T01:03:19.740962Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Contrast MRI Motion Correction via Parameter-Informed Disentanglement and Adaptive Experts","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Dinggang Shen, Feng Li, Honglin Xiong, Lei Xiang, Qian Wang, Yulin Wang, Yuxian Tang","submitted_at":"2026-05-29T02:48:23Z","abstract_excerpt":"Motion artifacts in magnetic resonance imaging (MRI) degrade diagnostic reliability. Existing deep learning methods are typically contrast-specific and fail to generalize across diverse modalities and artifact severities. We propose a unified framework combining parameter-informed contrast disentanglement with severity-aware adaptive correction. ScanCLIP, pretrained on over 30,000 MRI text-image pairs, derives contrast embeddings from acquisition parameters to disentangle contrast style from anatomical content, yielding contrast-free features. A Vision Transformer then estimates motion severit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.00146","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/2606.00146/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":"2606.00146","created_at":"2026-06-02T01:03:19.741011+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.00146v1","created_at":"2026-06-02T01:03:19.741011+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.00146","created_at":"2026-06-02T01:03:19.741011+00:00"},{"alias_kind":"pith_short_12","alias_value":"QG66LTT2KWKX","created_at":"2026-06-02T01:03:19.741011+00:00"},{"alias_kind":"pith_short_16","alias_value":"QG66LTT2KWKXEQMD","created_at":"2026-06-02T01:03:19.741011+00:00"},{"alias_kind":"pith_short_8","alias_value":"QG66LTT2","created_at":"2026-06-02T01:03:19.741011+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/QG66LTT2KWKXEQMDV4OD2YENUT","json":"https://pith.science/pith/QG66LTT2KWKXEQMDV4OD2YENUT.json","graph_json":"https://pith.science/api/pith-number/QG66LTT2KWKXEQMDV4OD2YENUT/graph.json","events_json":"https://pith.science/api/pith-number/QG66LTT2KWKXEQMDV4OD2YENUT/events.json","paper":"https://pith.science/paper/QG66LTT2"},"agent_actions":{"view_html":"https://pith.science/pith/QG66LTT2KWKXEQMDV4OD2YENUT","download_json":"https://pith.science/pith/QG66LTT2KWKXEQMDV4OD2YENUT.json","view_paper":"https://pith.science/paper/QG66LTT2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.00146&json=true","fetch_graph":"https://pith.science/api/pith-number/QG66LTT2KWKXEQMDV4OD2YENUT/graph.json","fetch_events":"https://pith.science/api/pith-number/QG66LTT2KWKXEQMDV4OD2YENUT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QG66LTT2KWKXEQMDV4OD2YENUT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QG66LTT2KWKXEQMDV4OD2YENUT/action/storage_attestation","attest_author":"https://pith.science/pith/QG66LTT2KWKXEQMDV4OD2YENUT/action/author_attestation","sign_citation":"https://pith.science/pith/QG66LTT2KWKXEQMDV4OD2YENUT/action/citation_signature","submit_replication":"https://pith.science/pith/QG66LTT2KWKXEQMDV4OD2YENUT/action/replication_record"}},"created_at":"2026-06-02T01:03:19.741011+00:00","updated_at":"2026-06-02T01:03:19.741011+00:00"}