{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:YTJ3NXTOVC5FXSV7B5NGLMSNLB","short_pith_number":"pith:YTJ3NXTO","canonical_record":{"source":{"id":"2507.13782","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-07-18T09:54:59Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"0c74601a0331ef66016e58e265ad3f130db64c985d0d7c2bd8fa2071da317495","abstract_canon_sha256":"7527134fcb6e426cd124de5900ae576dc628f1fa00dac5086ce49d03f930012e"},"schema_version":"1.0"},"canonical_sha256":"c4d3b6de6ea8ba5bcabf0f5a65b24d58521a9ecce7fe835ce2bbbabcb1619e33","source":{"kind":"arxiv","id":"2507.13782","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.13782","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"arxiv_version","alias_value":"2507.13782v1","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.13782","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"pith_short_12","alias_value":"YTJ3NXTOVC5F","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"pith_short_16","alias_value":"YTJ3NXTOVC5FXSV7","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"pith_short_8","alias_value":"YTJ3NXTO","created_at":"2026-07-05T11:39:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:YTJ3NXTOVC5FXSV7B5NGLMSNLB","target":"record","payload":{"canonical_record":{"source":{"id":"2507.13782","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-07-18T09:54:59Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"0c74601a0331ef66016e58e265ad3f130db64c985d0d7c2bd8fa2071da317495","abstract_canon_sha256":"7527134fcb6e426cd124de5900ae576dc628f1fa00dac5086ce49d03f930012e"},"schema_version":"1.0"},"canonical_sha256":"c4d3b6de6ea8ba5bcabf0f5a65b24d58521a9ecce7fe835ce2bbbabcb1619e33","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:23.057832Z","signature_b64":"EYMkGar37daykVQvs3EEL2Dg26osGZBDWZ/MwbhBD9qG3/IttKWKoxjPBBePnYsg3V3ZbmhE0cRsAWuAdTyPAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4d3b6de6ea8ba5bcabf0f5a65b24d58521a9ecce7fe835ce2bbbabcb1619e33","last_reissued_at":"2026-07-05T11:39:23.057390Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:23.057390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2507.13782","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:39:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+J9H0JnLTEBx6Ts1/+v8CMhmcEsZlHUOjB74iU3GWMTF7PlaMZ3xBZVluGFypyAnkMc167kgGRGNh3nTo8/kBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-14T17:28:54.340718Z"},"content_sha256":"94ad88b6e655c0fd1b089f20068d2265dc452c87ef706c4fe91b167a8b849a40","schema_version":"1.0","event_id":"sha256:94ad88b6e655c0fd1b089f20068d2265dc452c87ef706c4fe91b167a8b849a40"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:YTJ3NXTOVC5FXSV7B5NGLMSNLB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Converting T1-weighted MRI from 3T to 7T quality using deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Anika Wuestefeld, Danielle van Westen, David Berron, Gabrielle Flood, Jacob Vogel, Kalle {\\AA}str\\\"om, Laura EM Wisse, Malo Gicquel, Nicola Spotorno, Niklas Mattsson-Carlgren, Olof Strandberg, Oskar Hansson, Rik Ossenkoppele, Ruoyi Zhao, Yu Xiao","submitted_at":"2025-07-18T09:54:59Z","abstract_excerpt":"Ultra-high resolution 7 tesla (7T) magnetic resonance imaging (MRI) provides detailed anatomical views, offering better signal-to-noise ratio, resolution and tissue contrast than 3T MRI, though at the cost of accessibility. We present an advanced deep learning model for synthesizing 7T brain MRI from 3T brain MRI. Paired 7T and 3T T1-weighted images were acquired from 172 participants (124 cognitively unimpaired, 48 impaired) from the Swedish BioFINDER-2 study. To synthesize 7T MRI from 3T images, we trained two models: a specialized U-Net, and a U-Net integrated with a generative adversarial "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.13782","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/2507.13782/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:39:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"673E6VoCpjua47/ZfvJaghxiEf+39CPqr2JaYD8o9RRDfg5b6XPtoLUppPM8syxgGrOm9HIaWOOg57Kqf6iKBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-14T17:28:54.341106Z"},"content_sha256":"827abaf9105fbcd7264ed82cc1eb0ab0e5f4c345868a1b75fffb34512ba967b6","schema_version":"1.0","event_id":"sha256:827abaf9105fbcd7264ed82cc1eb0ab0e5f4c345868a1b75fffb34512ba967b6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YTJ3NXTOVC5FXSV7B5NGLMSNLB/bundle.json","state_url":"https://pith.science/pith/YTJ3NXTOVC5FXSV7B5NGLMSNLB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YTJ3NXTOVC5FXSV7B5NGLMSNLB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-14T17:28:54Z","links":{"resolver":"https://pith.science/pith/YTJ3NXTOVC5FXSV7B5NGLMSNLB","bundle":"https://pith.science/pith/YTJ3NXTOVC5FXSV7B5NGLMSNLB/bundle.json","state":"https://pith.science/pith/YTJ3NXTOVC5FXSV7B5NGLMSNLB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YTJ3NXTOVC5FXSV7B5NGLMSNLB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:YTJ3NXTOVC5FXSV7B5NGLMSNLB","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"7527134fcb6e426cd124de5900ae576dc628f1fa00dac5086ce49d03f930012e","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-07-18T09:54:59Z","title_canon_sha256":"0c74601a0331ef66016e58e265ad3f130db64c985d0d7c2bd8fa2071da317495"},"schema_version":"1.0","source":{"id":"2507.13782","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2507.13782","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"arxiv_version","alias_value":"2507.13782v1","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.13782","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"pith_short_12","alias_value":"YTJ3NXTOVC5F","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"pith_short_16","alias_value":"YTJ3NXTOVC5FXSV7","created_at":"2026-07-05T11:39:23Z"},{"alias_kind":"pith_short_8","alias_value":"YTJ3NXTO","created_at":"2026-07-05T11:39:23Z"}],"graph_snapshots":[{"event_id":"sha256:827abaf9105fbcd7264ed82cc1eb0ab0e5f4c345868a1b75fffb34512ba967b6","target":"graph","created_at":"2026-07-05T11:39:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2507.13782/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Ultra-high resolution 7 tesla (7T) magnetic resonance imaging (MRI) provides detailed anatomical views, offering better signal-to-noise ratio, resolution and tissue contrast than 3T MRI, though at the cost of accessibility. We present an advanced deep learning model for synthesizing 7T brain MRI from 3T brain MRI. Paired 7T and 3T T1-weighted images were acquired from 172 participants (124 cognitively unimpaired, 48 impaired) from the Swedish BioFINDER-2 study. To synthesize 7T MRI from 3T images, we trained two models: a specialized U-Net, and a U-Net integrated with a generative adversarial ","authors_text":"Anika Wuestefeld, Danielle van Westen, David Berron, Gabrielle Flood, Jacob Vogel, Kalle {\\AA}str\\\"om, Laura EM Wisse, Malo Gicquel, Nicola Spotorno, Niklas Mattsson-Carlgren, Olof Strandberg, Oskar Hansson, Rik Ossenkoppele, Ruoyi Zhao, Yu Xiao","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-07-18T09:54:59Z","title":"Converting T1-weighted MRI from 3T to 7T quality using deep learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.13782","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:94ad88b6e655c0fd1b089f20068d2265dc452c87ef706c4fe91b167a8b849a40","target":"record","created_at":"2026-07-05T11:39:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"7527134fcb6e426cd124de5900ae576dc628f1fa00dac5086ce49d03f930012e","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-07-18T09:54:59Z","title_canon_sha256":"0c74601a0331ef66016e58e265ad3f130db64c985d0d7c2bd8fa2071da317495"},"schema_version":"1.0","source":{"id":"2507.13782","kind":"arxiv","version":1}},"canonical_sha256":"c4d3b6de6ea8ba5bcabf0f5a65b24d58521a9ecce7fe835ce2bbbabcb1619e33","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c4d3b6de6ea8ba5bcabf0f5a65b24d58521a9ecce7fe835ce2bbbabcb1619e33","first_computed_at":"2026-07-05T11:39:23.057390Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:39:23.057390Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EYMkGar37daykVQvs3EEL2Dg26osGZBDWZ/MwbhBD9qG3/IttKWKoxjPBBePnYsg3V3ZbmhE0cRsAWuAdTyPAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:39:23.057832Z","signed_message":"canonical_sha256_bytes"},"source_id":"2507.13782","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:94ad88b6e655c0fd1b089f20068d2265dc452c87ef706c4fe91b167a8b849a40","sha256:827abaf9105fbcd7264ed82cc1eb0ab0e5f4c345868a1b75fffb34512ba967b6"],"state_sha256":"0be8ecde0ea4c3befd0fafd864423625a12d1801b2533659d8d8eced6d89c0c7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RmRvLPOI+/WQTn/1xrd7jYwjV9RkUyi0o8XkiyYZ8+Z2esb6W6inWKvW+BltDHohDKQ6KLpvyrZYNMHwc+WfCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-14T17:28:54.343845Z","bundle_sha256":"8dd4ddbbc5c9679e67459ce313ee5df06c06377f52a4451c8b07eaa47b4fd1ef"}}