{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:BB4LET3K6ZRA2C7H7ZMG3XTYCC","short_pith_number":"pith:BB4LET3K","canonical_record":{"source":{"id":"2307.10094","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-07-19T16:01:09Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"26e698a8a2fe4e0996232562781a007a492123314b5828653208b6a0ba4d5bc0","abstract_canon_sha256":"8430157f75552500f1c7ff1c2ddc18acef8c8f6af42cbdcf460ecb524c9f079c"},"schema_version":"1.0"},"canonical_sha256":"0878b24f6af6620d0be7fe586dde7810af93a1f8f3961533c95c3ab1d512566a","source":{"kind":"arxiv","id":"2307.10094","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.10094","created_at":"2026-07-05T06:32:50Z"},{"alias_kind":"arxiv_version","alias_value":"2307.10094v1","created_at":"2026-07-05T06:32:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.10094","created_at":"2026-07-05T06:32:50Z"},{"alias_kind":"pith_short_12","alias_value":"BB4LET3K6ZRA","created_at":"2026-07-05T06:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"BB4LET3K6ZRA2C7H","created_at":"2026-07-05T06:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"BB4LET3K","created_at":"2026-07-05T06:32:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:BB4LET3K6ZRA2C7H7ZMG3XTYCC","target":"record","payload":{"canonical_record":{"source":{"id":"2307.10094","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-07-19T16:01:09Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"26e698a8a2fe4e0996232562781a007a492123314b5828653208b6a0ba4d5bc0","abstract_canon_sha256":"8430157f75552500f1c7ff1c2ddc18acef8c8f6af42cbdcf460ecb524c9f079c"},"schema_version":"1.0"},"canonical_sha256":"0878b24f6af6620d0be7fe586dde7810af93a1f8f3961533c95c3ab1d512566a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:32:50.591112Z","signature_b64":"4Mlh4xYyRTvmkG4L+Xi2IHTwCx+L/Gsn2l1cowojXi0kGDbxFVXJffId41MYQOOELgFnnF2UOvjRLfvGRkhHBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0878b24f6af6620d0be7fe586dde7810af93a1f8f3961533c95c3ab1d512566a","last_reissued_at":"2026-07-05T06:32:50.590720Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:32:50.590720Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.10094","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":"2026-07-05T06:32:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rTD9Yz1znYsmigAxt5nA7ufe2QEzHvimbucuwpDvZmPYmTsa0N5SLoBTtARY+MMwYtNSxmK7qVwaqfbcKe1+BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T15:20:54.629404Z"},"content_sha256":"2b2e46f1a511dc8830d993ebb712b698e4ac3126fb891ffd00cbe29d99cd7425","schema_version":"1.0","event_id":"sha256:2b2e46f1a511dc8830d993ebb712b698e4ac3126fb891ffd00cbe29d99cd7425"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:BB4LET3K6ZRA2C7H7ZMG3XTYCC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Jingzhen He, Lequan Yu, Lingting Zhu, Xian Liu, Zeyue Xue, Zhenchao Jin, Ziwei Liu","submitted_at":"2023-07-19T16:01:09Z","abstract_excerpt":"Cross-modality medical image synthesis is a critical topic and has the potential to facilitate numerous applications in the medical imaging field. Despite recent successes in deep-learning-based generative models, most current medical image synthesis methods rely on generative adversarial networks and suffer from notorious mode collapse and unstable training. Moreover, the 2D backbone-driven approaches would easily result in volumetric inconsistency, while 3D backbones are challenging and impractical due to the tremendous memory cost and training difficulty. In this paper, we introduce a new p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.10094","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/2307.10094/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":"2026-07-05T06:32:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kjh09yZae3J6YC0lvq+CX3lfe/Nh71cVpuMlUvqQ86NVHdqcc7xqonIMf7R37FE7zfNHvtwG9yeKSSSkjm6TAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T15:20:54.630106Z"},"content_sha256":"bc912012996b24aa5be240152c105a4d580d3ef70710d051a19634f764b51773","schema_version":"1.0","event_id":"sha256:bc912012996b24aa5be240152c105a4d580d3ef70710d051a19634f764b51773"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BB4LET3K6ZRA2C7H7ZMG3XTYCC/bundle.json","state_url":"https://pith.science/pith/BB4LET3K6ZRA2C7H7ZMG3XTYCC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BB4LET3K6ZRA2C7H7ZMG3XTYCC/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":"2026-08-13T15:20:54Z","links":{"resolver":"https://pith.science/pith/BB4LET3K6ZRA2C7H7ZMG3XTYCC","bundle":"https://pith.science/pith/BB4LET3K6ZRA2C7H7ZMG3XTYCC/bundle.json","state":"https://pith.science/pith/BB4LET3K6ZRA2C7H7ZMG3XTYCC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BB4LET3K6ZRA2C7H7ZMG3XTYCC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:BB4LET3K6ZRA2C7H7ZMG3XTYCC","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":"8430157f75552500f1c7ff1c2ddc18acef8c8f6af42cbdcf460ecb524c9f079c","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-07-19T16:01:09Z","title_canon_sha256":"26e698a8a2fe4e0996232562781a007a492123314b5828653208b6a0ba4d5bc0"},"schema_version":"1.0","source":{"id":"2307.10094","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.10094","created_at":"2026-07-05T06:32:50Z"},{"alias_kind":"arxiv_version","alias_value":"2307.10094v1","created_at":"2026-07-05T06:32:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.10094","created_at":"2026-07-05T06:32:50Z"},{"alias_kind":"pith_short_12","alias_value":"BB4LET3K6ZRA","created_at":"2026-07-05T06:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"BB4LET3K6ZRA2C7H","created_at":"2026-07-05T06:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"BB4LET3K","created_at":"2026-07-05T06:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:bc912012996b24aa5be240152c105a4d580d3ef70710d051a19634f764b51773","target":"graph","created_at":"2026-07-05T06:32:50Z","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/2307.10094/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Cross-modality medical image synthesis is a critical topic and has the potential to facilitate numerous applications in the medical imaging field. Despite recent successes in deep-learning-based generative models, most current medical image synthesis methods rely on generative adversarial networks and suffer from notorious mode collapse and unstable training. Moreover, the 2D backbone-driven approaches would easily result in volumetric inconsistency, while 3D backbones are challenging and impractical due to the tremendous memory cost and training difficulty. In this paper, we introduce a new p","authors_text":"Jingzhen He, Lequan Yu, Lingting Zhu, Xian Liu, Zeyue Xue, Zhenchao Jin, Ziwei Liu","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-07-19T16:01:09Z","title":"Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.10094","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":"sha256:2b2e46f1a511dc8830d993ebb712b698e4ac3126fb891ffd00cbe29d99cd7425","target":"record","created_at":"2026-07-05T06:32:50Z","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":"8430157f75552500f1c7ff1c2ddc18acef8c8f6af42cbdcf460ecb524c9f079c","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-07-19T16:01:09Z","title_canon_sha256":"26e698a8a2fe4e0996232562781a007a492123314b5828653208b6a0ba4d5bc0"},"schema_version":"1.0","source":{"id":"2307.10094","kind":"arxiv","version":1}},"canonical_sha256":"0878b24f6af6620d0be7fe586dde7810af93a1f8f3961533c95c3ab1d512566a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0878b24f6af6620d0be7fe586dde7810af93a1f8f3961533c95c3ab1d512566a","first_computed_at":"2026-07-05T06:32:50.590720Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:32:50.590720Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4Mlh4xYyRTvmkG4L+Xi2IHTwCx+L/Gsn2l1cowojXi0kGDbxFVXJffId41MYQOOELgFnnF2UOvjRLfvGRkhHBg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:32:50.591112Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.10094","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b2e46f1a511dc8830d993ebb712b698e4ac3126fb891ffd00cbe29d99cd7425","sha256:bc912012996b24aa5be240152c105a4d580d3ef70710d051a19634f764b51773"],"state_sha256":"9f1c65cdbd52a57a7ca44a2ee80e876cf201e4efe48e3b098fdd63a54eb6e862"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Cx/wclzMC6NAdN3G5892zaNmZOECd5SxvXRWsOrzqcdN50SNkyCYt87rPsTvpprtb9Hw7zysnt+Zp02RmECGBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T15:20:54.636635Z","bundle_sha256":"1d1d573333513bb0ce01cce7b04292c53f7bee98a8bff6c1efbab9fc57ef7f38"}}