{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:RFEP4CJK5H72BSZNNWW5IMASX5","short_pith_number":"pith:RFEP4CJK","canonical_record":{"source":{"id":"1812.11440","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-29T22:19:00Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"122b183cc7477f1a1023d57bdf763266c4a1fa1c2c093517381b6c4fd6dc681a","abstract_canon_sha256":"86a729b1e014f899b3f467dea4fd70591b8151a6e7f53977b8fa64df619c9a90"},"schema_version":"1.0"},"canonical_sha256":"8948fe092ae9ffa0cb2d6dadd43012bf6a2516c226444832d54d905f13da7d0b","source":{"kind":"arxiv","id":"1812.11440","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.11440","created_at":"2026-05-17T23:57:11Z"},{"alias_kind":"arxiv_version","alias_value":"1812.11440v1","created_at":"2026-05-17T23:57:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.11440","created_at":"2026-05-17T23:57:11Z"},{"alias_kind":"pith_short_12","alias_value":"RFEP4CJK5H72","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RFEP4CJK5H72BSZN","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RFEP4CJK","created_at":"2026-05-18T12:32:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:RFEP4CJK5H72BSZNNWW5IMASX5","target":"record","payload":{"canonical_record":{"source":{"id":"1812.11440","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-29T22:19:00Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"122b183cc7477f1a1023d57bdf763266c4a1fa1c2c093517381b6c4fd6dc681a","abstract_canon_sha256":"86a729b1e014f899b3f467dea4fd70591b8151a6e7f53977b8fa64df619c9a90"},"schema_version":"1.0"},"canonical_sha256":"8948fe092ae9ffa0cb2d6dadd43012bf6a2516c226444832d54d905f13da7d0b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:11.954449Z","signature_b64":"pvW2IsrKt5WWBjJtJI0mqHrw92Ifn7i9X+7qJOmSinmth84zT3jeYOA8OBANk4NicZlHB9m7ksizN+7ee1k2AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8948fe092ae9ffa0cb2d6dadd43012bf6a2516c226444832d54d905f13da7d0b","last_reissued_at":"2026-05-17T23:57:11.953996Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:11.953996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.11440","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":"2026-05-17T23:57:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iBpmhqOGXNPwUo46QvxR43zDwXs8JfY6dNxrcVFH+t47ZqRmtX7kOfQELVrzj8ycBjq+UiOkPdxej26PmIWCDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T17:17:53.808941Z"},"content_sha256":"062e1d1f7ba189b04227602a9855f7e5a35d6accbfee92d93396fef629f13a0e","schema_version":"1.0","event_id":"sha256:062e1d1f7ba189b04227602a9855f7e5a35d6accbfee92d93396fef629f13a0e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:RFEP4CJK5H72BSZNNWW5IMASX5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Brain MRI super-resolution using 3D generative adversarial networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Irina Sanchez, Veronica Vilaplana","submitted_at":"2018-12-29T22:19:00Z","abstract_excerpt":"In this work we propose an adversarial learning approach to generate high resolution MRI scans from low resolution images. The architecture, based on the SRGAN model, adopts 3D convolutions to exploit volumetric information. For the discriminator, the adversarial loss uses least squares in order to stabilize the training. For the generator, the loss function is a combination of a least squares adversarial loss and a content term based on mean square error and image gradients in order to improve the quality of the generated images. We explore different solutions for the upsampling phase. We pre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.11440","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":""},"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":"2026-05-17T23:57:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qcHEu1DLpxVxBTuuBKIgWQjiirLDvIuG0VSOfg8j9G/PwT7W9yMmfUbjbcfE/RTCTZ6dbgjPqrDxAQ50khslCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T17:17:53.809753Z"},"content_sha256":"f2a8ea4339d598e65db8fe983586a7d66de5448afa98a35b6c0d06f79216d180","schema_version":"1.0","event_id":"sha256:f2a8ea4339d598e65db8fe983586a7d66de5448afa98a35b6c0d06f79216d180"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RFEP4CJK5H72BSZNNWW5IMASX5/bundle.json","state_url":"https://pith.science/pith/RFEP4CJK5H72BSZNNWW5IMASX5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RFEP4CJK5H72BSZNNWW5IMASX5/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":"2026-05-31T17:17:53Z","links":{"resolver":"https://pith.science/pith/RFEP4CJK5H72BSZNNWW5IMASX5","bundle":"https://pith.science/pith/RFEP4CJK5H72BSZNNWW5IMASX5/bundle.json","state":"https://pith.science/pith/RFEP4CJK5H72BSZNNWW5IMASX5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RFEP4CJK5H72BSZNNWW5IMASX5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:RFEP4CJK5H72BSZNNWW5IMASX5","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":"86a729b1e014f899b3f467dea4fd70591b8151a6e7f53977b8fa64df619c9a90","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-29T22:19:00Z","title_canon_sha256":"122b183cc7477f1a1023d57bdf763266c4a1fa1c2c093517381b6c4fd6dc681a"},"schema_version":"1.0","source":{"id":"1812.11440","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.11440","created_at":"2026-05-17T23:57:11Z"},{"alias_kind":"arxiv_version","alias_value":"1812.11440v1","created_at":"2026-05-17T23:57:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.11440","created_at":"2026-05-17T23:57:11Z"},{"alias_kind":"pith_short_12","alias_value":"RFEP4CJK5H72","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RFEP4CJK5H72BSZN","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RFEP4CJK","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:f2a8ea4339d598e65db8fe983586a7d66de5448afa98a35b6c0d06f79216d180","target":"graph","created_at":"2026-05-17T23:57:11Z","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"},"paper":{"abstract_excerpt":"In this work we propose an adversarial learning approach to generate high resolution MRI scans from low resolution images. The architecture, based on the SRGAN model, adopts 3D convolutions to exploit volumetric information. For the discriminator, the adversarial loss uses least squares in order to stabilize the training. For the generator, the loss function is a combination of a least squares adversarial loss and a content term based on mean square error and image gradients in order to improve the quality of the generated images. We explore different solutions for the upsampling phase. We pre","authors_text":"Irina Sanchez, Veronica Vilaplana","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-29T22:19:00Z","title":"Brain MRI super-resolution using 3D generative adversarial networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.11440","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":"sha256:062e1d1f7ba189b04227602a9855f7e5a35d6accbfee92d93396fef629f13a0e","target":"record","created_at":"2026-05-17T23:57:11Z","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":"86a729b1e014f899b3f467dea4fd70591b8151a6e7f53977b8fa64df619c9a90","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-12-29T22:19:00Z","title_canon_sha256":"122b183cc7477f1a1023d57bdf763266c4a1fa1c2c093517381b6c4fd6dc681a"},"schema_version":"1.0","source":{"id":"1812.11440","kind":"arxiv","version":1}},"canonical_sha256":"8948fe092ae9ffa0cb2d6dadd43012bf6a2516c226444832d54d905f13da7d0b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8948fe092ae9ffa0cb2d6dadd43012bf6a2516c226444832d54d905f13da7d0b","first_computed_at":"2026-05-17T23:57:11.953996Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:11.953996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pvW2IsrKt5WWBjJtJI0mqHrw92Ifn7i9X+7qJOmSinmth84zT3jeYOA8OBANk4NicZlHB9m7ksizN+7ee1k2AQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:11.954449Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.11440","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:062e1d1f7ba189b04227602a9855f7e5a35d6accbfee92d93396fef629f13a0e","sha256:f2a8ea4339d598e65db8fe983586a7d66de5448afa98a35b6c0d06f79216d180"],"state_sha256":"70590336d300c8f735f4333e37ada6111e7f5113f468128e8f7bc9ca9611fc79"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w2kZY49QdMGDGqR6+77ploaUmxGtRlddt9RtPTZtUSkYPqfYNVaWCpQq/N4N5Ri1xDiGJVmEjP+Mw6tWOb1ZDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T17:17:53.813989Z","bundle_sha256":"e84499e97fe6597e387a2ceb81a132d398ac7289a033a67809320a63e2aac9ba"}}