{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:Y5H26VFO5ESLDO6IC6JQP6LZTW","short_pith_number":"pith:Y5H26VFO","schema_version":"1.0","canonical_sha256":"c74faf54aee924b1bbc8179307f9799daaa4b25fd8912ac3e78ac7d578d623ef","source":{"kind":"arxiv","id":"2506.03183","version":1},"attestation_state":"computed","paper":{"title":"Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.AR","cs.CV","cs.LG","physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Mehmet Ak\\c{c}akaya, Ya\\c{s}ar Utku Al\\c{c}alar, Yu Cao","submitted_at":"2025-05-30T02:35:43Z","abstract_excerpt":"Physics-driven artificial intelligence (PD-AI) reconstruction methods have emerged as the state-of-the-art for accelerating MRI scans, enabling higher spatial and temporal resolutions. However, the high resolution of these scans generates massive data volumes, leading to challenges in transmission, storage, and real-time processing. This is particularly pronounced in functional MRI, where hundreds of volumetric acquisitions further exacerbate these demands. Edge computing with FPGAs presents a promising solution for enabling PD-AI reconstruction near the MRI sensors, reducing data transfer and"},"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":"2506.03183","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2025-05-30T02:35:43Z","cross_cats_sorted":["cs.AI","cs.AR","cs.CV","cs.LG","physics.med-ph"],"title_canon_sha256":"d4e6678a960d1512c9fbab7e1ac441a5136bdd9018f8b20e25fa1482fdbfac7c","abstract_canon_sha256":"e74440f18057f198e80cc8cf16d2382c59dc33a385fa2b159aed2f6005fa8bd9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:15:22.664225Z","signature_b64":"AOK2g9xdTEZ+5zV8eXKYMb1lhm4dx02sL5JC2+8eiExKW0DLZ1MWaPkVjYZ92ETE4zC7/nAUqDI2jDW3slz3BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c74faf54aee924b1bbc8179307f9799daaa4b25fd8912ac3e78ac7d578d623ef","last_reissued_at":"2026-07-05T11:15:22.663712Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:15:22.663712Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.AR","cs.CV","cs.LG","physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Mehmet Ak\\c{c}akaya, Ya\\c{s}ar Utku Al\\c{c}alar, Yu Cao","submitted_at":"2025-05-30T02:35:43Z","abstract_excerpt":"Physics-driven artificial intelligence (PD-AI) reconstruction methods have emerged as the state-of-the-art for accelerating MRI scans, enabling higher spatial and temporal resolutions. However, the high resolution of these scans generates massive data volumes, leading to challenges in transmission, storage, and real-time processing. This is particularly pronounced in functional MRI, where hundreds of volumetric acquisitions further exacerbate these demands. Edge computing with FPGAs presents a promising solution for enabling PD-AI reconstruction near the MRI sensors, reducing data transfer and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.03183","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/2506.03183/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":"2506.03183","created_at":"2026-07-05T11:15:22.663781+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.03183v1","created_at":"2026-07-05T11:15:22.663781+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.03183","created_at":"2026-07-05T11:15:22.663781+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y5H26VFO5ESL","created_at":"2026-07-05T11:15:22.663781+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y5H26VFO5ESLDO6I","created_at":"2026-07-05T11:15:22.663781+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y5H26VFO","created_at":"2026-07-05T11:15:22.663781+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/Y5H26VFO5ESLDO6IC6JQP6LZTW","json":"https://pith.science/pith/Y5H26VFO5ESLDO6IC6JQP6LZTW.json","graph_json":"https://pith.science/api/pith-number/Y5H26VFO5ESLDO6IC6JQP6LZTW/graph.json","events_json":"https://pith.science/api/pith-number/Y5H26VFO5ESLDO6IC6JQP6LZTW/events.json","paper":"https://pith.science/paper/Y5H26VFO"},"agent_actions":{"view_html":"https://pith.science/pith/Y5H26VFO5ESLDO6IC6JQP6LZTW","download_json":"https://pith.science/pith/Y5H26VFO5ESLDO6IC6JQP6LZTW.json","view_paper":"https://pith.science/paper/Y5H26VFO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.03183&json=true","fetch_graph":"https://pith.science/api/pith-number/Y5H26VFO5ESLDO6IC6JQP6LZTW/graph.json","fetch_events":"https://pith.science/api/pith-number/Y5H26VFO5ESLDO6IC6JQP6LZTW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y5H26VFO5ESLDO6IC6JQP6LZTW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y5H26VFO5ESLDO6IC6JQP6LZTW/action/storage_attestation","attest_author":"https://pith.science/pith/Y5H26VFO5ESLDO6IC6JQP6LZTW/action/author_attestation","sign_citation":"https://pith.science/pith/Y5H26VFO5ESLDO6IC6JQP6LZTW/action/citation_signature","submit_replication":"https://pith.science/pith/Y5H26VFO5ESLDO6IC6JQP6LZTW/action/replication_record"}},"created_at":"2026-07-05T11:15:22.663781+00:00","updated_at":"2026-07-05T11:15:22.663781+00:00"}