{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:XZ4VTTFNCGZR4I5WWXFYJYT5E2","short_pith_number":"pith:XZ4VTTFN","canonical_record":{"source":{"id":"2108.02160","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-08-04T16:38:33Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"87d80bf7d3428f78b4d2c11695f86a97c6c4ecb42b602130de6776741ab8ed4f","abstract_canon_sha256":"93f72c4a7242e2130991c531030764e00843759e5bcba8175e33790f790a2289"},"schema_version":"1.0"},"canonical_sha256":"be7959ccad11b31e23b6b5cb84e27d269b6e02a79fd8fa54725153a258340bac","source":{"kind":"arxiv","id":"2108.02160","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.02160","created_at":"2026-07-05T09:47:53Z"},{"alias_kind":"arxiv_version","alias_value":"2108.02160v2","created_at":"2026-07-05T09:47:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.02160","created_at":"2026-07-05T09:47:53Z"},{"alias_kind":"pith_short_12","alias_value":"XZ4VTTFNCGZR","created_at":"2026-07-05T09:47:53Z"},{"alias_kind":"pith_short_16","alias_value":"XZ4VTTFNCGZR4I5W","created_at":"2026-07-05T09:47:53Z"},{"alias_kind":"pith_short_8","alias_value":"XZ4VTTFN","created_at":"2026-07-05T09:47:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:XZ4VTTFNCGZR4I5WWXFYJYT5E2","target":"record","payload":{"canonical_record":{"source":{"id":"2108.02160","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-08-04T16:38:33Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"87d80bf7d3428f78b4d2c11695f86a97c6c4ecb42b602130de6776741ab8ed4f","abstract_canon_sha256":"93f72c4a7242e2130991c531030764e00843759e5bcba8175e33790f790a2289"},"schema_version":"1.0"},"canonical_sha256":"be7959ccad11b31e23b6b5cb84e27d269b6e02a79fd8fa54725153a258340bac","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:47:53.197575Z","signature_b64":"Z5hlKhnKtKDPLI3l5/wXKymDnWBYXnMrQ2nsjZTfbcG6B6P599Dk8WktvbgaRf2wVeNXid/SA+Ed5ZmiN3rkDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"be7959ccad11b31e23b6b5cb84e27d269b6e02a79fd8fa54725153a258340bac","last_reissued_at":"2026-07-05T09:47:53.197085Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:47:53.197085Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2108.02160","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:47:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L2pUpv8svg1stOVV1spGOtyAmr6VWEGHdWb1iSooVF9Qfg82u6o0EXl0zwrBkImezX+TLrV7tQxJ/N65c6NcBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T14:50:34.815905Z"},"content_sha256":"286d179c3a2bbcb71e5afbe9a3fdebb11ab0001023a738fc877859d629314628","schema_version":"1.0","event_id":"sha256:286d179c3a2bbcb71e5afbe9a3fdebb11ab0001023a738fc877859d629314628"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:XZ4VTTFNCGZR4I5WWXFYJYT5E2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Apoorva Sikka, Deepti R. Bathula, Jitender Singh Virk, Skand Peri, Usma Niyaz","submitted_at":"2021-08-04T16:38:33Z","abstract_excerpt":"Medical imaging datasets are inherently high dimensional with large variability and low sample sizes that limit the effectiveness of deep learning algorithms. Recently, generative adversarial networks (GANs) with the ability to synthesize realist images have shown great potential as an alternative to standard data augmentation techniques. Our work focuses on cross-modality synthesis of fluorodeoxyglucose~(FDG) Positron Emission Tomography~(PET) scans from structural Magnetic Resonance~(MR) images using generative models to facilitate multi-modal diagnosis of Alzheimer's disease (AD). Specifica"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.02160","kind":"arxiv","version":2},"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/2108.02160/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":"2026-07-05T09:47:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w0ttQrQRIILt9BCAy3N2zWnvpRxeU/SoYDxcmA6V2VRcyFVP8vPO1IBqYFUk5hzWy74647J2QQyL7mYxMEN9Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T14:50:34.816308Z"},"content_sha256":"bd54d30625c0b5cb3a8ada14c37840401b935ce85e9c3def1a2f2b8ca61caa8e","schema_version":"1.0","event_id":"sha256:bd54d30625c0b5cb3a8ada14c37840401b935ce85e9c3def1a2f2b8ca61caa8e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XZ4VTTFNCGZR4I5WWXFYJYT5E2/bundle.json","state_url":"https://pith.science/pith/XZ4VTTFNCGZR4I5WWXFYJYT5E2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XZ4VTTFNCGZR4I5WWXFYJYT5E2/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":"2026-08-13T14:50:34Z","links":{"resolver":"https://pith.science/pith/XZ4VTTFNCGZR4I5WWXFYJYT5E2","bundle":"https://pith.science/pith/XZ4VTTFNCGZR4I5WWXFYJYT5E2/bundle.json","state":"https://pith.science/pith/XZ4VTTFNCGZR4I5WWXFYJYT5E2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XZ4VTTFNCGZR4I5WWXFYJYT5E2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:XZ4VTTFNCGZR4I5WWXFYJYT5E2","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":"93f72c4a7242e2130991c531030764e00843759e5bcba8175e33790f790a2289","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-08-04T16:38:33Z","title_canon_sha256":"87d80bf7d3428f78b4d2c11695f86a97c6c4ecb42b602130de6776741ab8ed4f"},"schema_version":"1.0","source":{"id":"2108.02160","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.02160","created_at":"2026-07-05T09:47:53Z"},{"alias_kind":"arxiv_version","alias_value":"2108.02160v2","created_at":"2026-07-05T09:47:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.02160","created_at":"2026-07-05T09:47:53Z"},{"alias_kind":"pith_short_12","alias_value":"XZ4VTTFNCGZR","created_at":"2026-07-05T09:47:53Z"},{"alias_kind":"pith_short_16","alias_value":"XZ4VTTFNCGZR4I5W","created_at":"2026-07-05T09:47:53Z"},{"alias_kind":"pith_short_8","alias_value":"XZ4VTTFN","created_at":"2026-07-05T09:47:53Z"}],"graph_snapshots":[{"event_id":"sha256:bd54d30625c0b5cb3a8ada14c37840401b935ce85e9c3def1a2f2b8ca61caa8e","target":"graph","created_at":"2026-07-05T09:47:53Z","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/2108.02160/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical imaging datasets are inherently high dimensional with large variability and low sample sizes that limit the effectiveness of deep learning algorithms. Recently, generative adversarial networks (GANs) with the ability to synthesize realist images have shown great potential as an alternative to standard data augmentation techniques. Our work focuses on cross-modality synthesis of fluorodeoxyglucose~(FDG) Positron Emission Tomography~(PET) scans from structural Magnetic Resonance~(MR) images using generative models to facilitate multi-modal diagnosis of Alzheimer's disease (AD). Specifica","authors_text":"Apoorva Sikka, Deepti R. Bathula, Jitender Singh Virk, Skand Peri, Usma Niyaz","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-08-04T16:38:33Z","title":"MRI to PET Cross-Modality Translation using Globally and Locally Aware GAN (GLA-GAN) for Multi-Modal Diagnosis of Alzheimer's Disease"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.02160","kind":"arxiv","version":2},"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":"sha256:286d179c3a2bbcb71e5afbe9a3fdebb11ab0001023a738fc877859d629314628","target":"record","created_at":"2026-07-05T09:47:53Z","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":"93f72c4a7242e2130991c531030764e00843759e5bcba8175e33790f790a2289","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2021-08-04T16:38:33Z","title_canon_sha256":"87d80bf7d3428f78b4d2c11695f86a97c6c4ecb42b602130de6776741ab8ed4f"},"schema_version":"1.0","source":{"id":"2108.02160","kind":"arxiv","version":2}},"canonical_sha256":"be7959ccad11b31e23b6b5cb84e27d269b6e02a79fd8fa54725153a258340bac","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"be7959ccad11b31e23b6b5cb84e27d269b6e02a79fd8fa54725153a258340bac","first_computed_at":"2026-07-05T09:47:53.197085Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:47:53.197085Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z5hlKhnKtKDPLI3l5/wXKymDnWBYXnMrQ2nsjZTfbcG6B6P599Dk8WktvbgaRf2wVeNXid/SA+Ed5ZmiN3rkDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:47:53.197575Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.02160","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:286d179c3a2bbcb71e5afbe9a3fdebb11ab0001023a738fc877859d629314628","sha256:bd54d30625c0b5cb3a8ada14c37840401b935ce85e9c3def1a2f2b8ca61caa8e"],"state_sha256":"89ce1e6ac54d4680559113e83dbdd3465e34f9fbcfb6b4e5e9fd986f53abc932"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zkBZqNFi5tof+Or6XCINoVEoQ6InxTQLp7u+pXhZHqzMMA6pmRjs/PsvO21m2Eq2GDvxc371KMtifMGceiQDAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T14:50:34.823465Z","bundle_sha256":"221f14619aa8a4b37125941773d0cfdedb7cdfb50d45ee82c2e776fab3d0dba9"}}