{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:YYUIGAWD5DUCOLO2OMF4JLBBCT","short_pith_number":"pith:YYUIGAWD","canonical_record":{"source":{"id":"2604.10245","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-11T14:58:45Z","cross_cats_sorted":["physics.med-ph"],"title_canon_sha256":"00f63f46f2c6f3dc2d0f9c3de92dd1faa4b4a7f651a1b1dd32af409a123169a7","abstract_canon_sha256":"ee9d40fb134b93f8d65eeb3b59cd6facb57342c0a5c15ca99a3538b1429656bd"},"schema_version":"1.0"},"canonical_sha256":"c6288302c3e8e8272dda730bc4ac2114de83365a693a9db42a4f48310bbb6515","source":{"kind":"arxiv","id":"2604.10245","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.10245","created_at":"2026-05-21T01:05:18Z"},{"alias_kind":"arxiv_version","alias_value":"2604.10245v2","created_at":"2026-05-21T01:05:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.10245","created_at":"2026-05-21T01:05:18Z"},{"alias_kind":"pith_short_12","alias_value":"YYUIGAWD5DUC","created_at":"2026-05-21T01:05:18Z"},{"alias_kind":"pith_short_16","alias_value":"YYUIGAWD5DUCOLO2","created_at":"2026-05-21T01:05:18Z"},{"alias_kind":"pith_short_8","alias_value":"YYUIGAWD","created_at":"2026-05-21T01:05:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:YYUIGAWD5DUCOLO2OMF4JLBBCT","target":"record","payload":{"canonical_record":{"source":{"id":"2604.10245","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-11T14:58:45Z","cross_cats_sorted":["physics.med-ph"],"title_canon_sha256":"00f63f46f2c6f3dc2d0f9c3de92dd1faa4b4a7f651a1b1dd32af409a123169a7","abstract_canon_sha256":"ee9d40fb134b93f8d65eeb3b59cd6facb57342c0a5c15ca99a3538b1429656bd"},"schema_version":"1.0"},"canonical_sha256":"c6288302c3e8e8272dda730bc4ac2114de83365a693a9db42a4f48310bbb6515","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:05:18.811565Z","signature_b64":"y8v8fu7ilNQag/cexqMeN4h6cBJ5+MNMgsKLI6P/pgxWBe9eugMLZG1Mz/Fa5ytybGnBKSC+nDLSPKpmhTsbAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6288302c3e8e8272dda730bc4ac2114de83365a693a9db42a4f48310bbb6515","last_reissued_at":"2026-05-21T01:05:18.811077Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:05:18.811077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.10245","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":"2026-05-21T01:05:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QflIjOPEkMA2WtXThVd5ld1m0ciG0ULtGq6F3Gpy4tK8bYqvBkn4dfm/mZhF0OtAg2Wv9hoz6HLmQQNOBshoBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T20:35:08.248214Z"},"content_sha256":"96de47625817ada76cd0b5a9fb05bbb544f8a88c3a977a19410daa2ea7221e84","schema_version":"1.0","event_id":"sha256:96de47625817ada76cd0b5a9fb05bbb544f8a88c3a977a19410daa2ea7221e84"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:YYUIGAWD5DUCOLO2OMF4JLBBCT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Warm-Started Reinforcement Learning for Iterative 3D/2D Liver Registration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A warm-started RL policy performs iterative 6-DoF CT-to-video liver registration and learns its own stopping criterion, reaching 15.70 mm TRE without extra optimization.","cross_cats":["physics.med-ph"],"primary_cat":"cs.CV","authors_text":"Abdolrahim Kadkhodamohammadi, Brian R. Davidson, Danail Stoyanov, Evangelos B. Mazomenos, Hanyuan Zhang, Lucas He, Matthew.J Clarkson, Zijie Cheng","submitted_at":"2026-04-11T14:58:45Z","abstract_excerpt":"Registration between preoperative CT and intraoperative laparoscopic video plays a crucial role in augmented reality (AR) guidance for minimally invasive surgery. Learning-based methods have recently achieved registration errors comparable to optimization-based approaches while offering faster inference. However, many supervised methods produce coarse alignments that rely on additional optimization-based refinement, thereby increasing inference time.\n  We present a discrete-action reinforcement learning (RL) framework that formulates CT-to-video registration as a sequential decision-making pro"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experiments on a public laparoscopic dataset demonstrated that our method achieved an average target registration error (TRE) of 15.70 mm, comparable to supervised approaches with optimization, while achieving faster convergence.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The warm-started RL policy can reliably learn effective 6-DoF rigid transformations and a stopping criterion from the shared encoder features without post-hoc tuning or overfitting to the specific dataset.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A warm-started discrete-action RL framework for CT-to-video liver registration achieves 15.70 mm average TRE with faster convergence than supervised methods plus optimization.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A warm-started RL policy performs iterative 6-DoF CT-to-video liver registration and learns its own stopping criterion, reaching 15.70 mm TRE without extra optimization.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"771a98c4eccbca92b4dbc402172d845fef535e5af946b0c296ca0731b934d31e"},"source":{"id":"2604.10245","kind":"arxiv","version":2},"verdict":{"id":"e9b7ba05-1384-4ca4-9e96-69e158994c22","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T16:28:53.192594Z","strongest_claim":"Experiments on a public laparoscopic dataset demonstrated that our method achieved an average target registration error (TRE) of 15.70 mm, comparable to supervised approaches with optimization, while achieving faster convergence.","one_line_summary":"A warm-started discrete-action RL framework for CT-to-video liver registration achieves 15.70 mm average TRE with faster convergence than supervised methods plus optimization.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The warm-started RL policy can reliably learn effective 6-DoF rigid transformations and a stopping criterion from the shared encoder features without post-hoc tuning or overfitting to the specific dataset.","pith_extraction_headline":"A warm-started RL policy performs iterative 6-DoF CT-to-video liver registration and learns its own stopping criterion, reaching 15.70 mm TRE without extra optimization."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.10245/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":"e9b7ba05-1384-4ca4-9e96-69e158994c22"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-21T01:05:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1soiCOPFeIdlmdXkmulKWE7Xo1IsmBLmMPTCbZMVybU14zsfHtcjhhVHvc7q7+NNX+Xxe3ekP4HbQV5yamLzBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T20:35:08.249099Z"},"content_sha256":"e64ef4682c0603b0386b989956a665d76308a0e048fc5f217022e5ec95272c27","schema_version":"1.0","event_id":"sha256:e64ef4682c0603b0386b989956a665d76308a0e048fc5f217022e5ec95272c27"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT/bundle.json","state_url":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT/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":"2026-05-25T20:35:08Z","links":{"resolver":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT","bundle":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT/bundle.json","state":"https://pith.science/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YYUIGAWD5DUCOLO2OMF4JLBBCT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:YYUIGAWD5DUCOLO2OMF4JLBBCT","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":"ee9d40fb134b93f8d65eeb3b59cd6facb57342c0a5c15ca99a3538b1429656bd","cross_cats_sorted":["physics.med-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-11T14:58:45Z","title_canon_sha256":"00f63f46f2c6f3dc2d0f9c3de92dd1faa4b4a7f651a1b1dd32af409a123169a7"},"schema_version":"1.0","source":{"id":"2604.10245","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.10245","created_at":"2026-05-21T01:05:18Z"},{"alias_kind":"arxiv_version","alias_value":"2604.10245v2","created_at":"2026-05-21T01:05:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.10245","created_at":"2026-05-21T01:05:18Z"},{"alias_kind":"pith_short_12","alias_value":"YYUIGAWD5DUC","created_at":"2026-05-21T01:05:18Z"},{"alias_kind":"pith_short_16","alias_value":"YYUIGAWD5DUCOLO2","created_at":"2026-05-21T01:05:18Z"},{"alias_kind":"pith_short_8","alias_value":"YYUIGAWD","created_at":"2026-05-21T01:05:18Z"}],"graph_snapshots":[{"event_id":"sha256:e64ef4682c0603b0386b989956a665d76308a0e048fc5f217022e5ec95272c27","target":"graph","created_at":"2026-05-21T01:05:18Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Experiments on a public laparoscopic dataset demonstrated that our method achieved an average target registration error (TRE) of 15.70 mm, comparable to supervised approaches with optimization, while achieving faster convergence."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The warm-started RL policy can reliably learn effective 6-DoF rigid transformations and a stopping criterion from the shared encoder features without post-hoc tuning or overfitting to the specific dataset."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A warm-started discrete-action RL framework for CT-to-video liver registration achieves 15.70 mm average TRE with faster convergence than supervised methods plus optimization."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A warm-started RL policy performs iterative 6-DoF CT-to-video liver registration and learns its own stopping criterion, reaching 15.70 mm TRE without extra optimization."}],"snapshot_sha256":"771a98c4eccbca92b4dbc402172d845fef535e5af946b0c296ca0731b934d31e"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.10245/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Registration between preoperative CT and intraoperative laparoscopic video plays a crucial role in augmented reality (AR) guidance for minimally invasive surgery. Learning-based methods have recently achieved registration errors comparable to optimization-based approaches while offering faster inference. However, many supervised methods produce coarse alignments that rely on additional optimization-based refinement, thereby increasing inference time.\n  We present a discrete-action reinforcement learning (RL) framework that formulates CT-to-video registration as a sequential decision-making pro","authors_text":"Abdolrahim Kadkhodamohammadi, Brian R. Davidson, Danail Stoyanov, Evangelos B. Mazomenos, Hanyuan Zhang, Lucas He, Matthew.J Clarkson, Zijie Cheng","cross_cats":["physics.med-ph"],"headline":"A warm-started RL policy performs iterative 6-DoF CT-to-video liver registration and learns its own stopping criterion, reaching 15.70 mm TRE without extra optimization.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-11T14:58:45Z","title":"Warm-Started Reinforcement Learning for Iterative 3D/2D Liver Registration"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.10245","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T16:28:53.192594Z","id":"e9b7ba05-1384-4ca4-9e96-69e158994c22","model_set":{"reader":"grok-4.3"},"one_line_summary":"A warm-started discrete-action RL framework for CT-to-video liver registration achieves 15.70 mm average TRE with faster convergence than supervised methods plus optimization.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A warm-started RL policy performs iterative 6-DoF CT-to-video liver registration and learns its own stopping criterion, reaching 15.70 mm TRE without extra optimization.","strongest_claim":"Experiments on a public laparoscopic dataset demonstrated that our method achieved an average target registration error (TRE) of 15.70 mm, comparable to supervised approaches with optimization, while achieving faster convergence.","weakest_assumption":"The warm-started RL policy can reliably learn effective 6-DoF rigid transformations and a stopping criterion from the shared encoder features without post-hoc tuning or overfitting to the specific dataset."}},"verdict_id":"e9b7ba05-1384-4ca4-9e96-69e158994c22"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:96de47625817ada76cd0b5a9fb05bbb544f8a88c3a977a19410daa2ea7221e84","target":"record","created_at":"2026-05-21T01:05:18Z","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":"ee9d40fb134b93f8d65eeb3b59cd6facb57342c0a5c15ca99a3538b1429656bd","cross_cats_sorted":["physics.med-ph"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-11T14:58:45Z","title_canon_sha256":"00f63f46f2c6f3dc2d0f9c3de92dd1faa4b4a7f651a1b1dd32af409a123169a7"},"schema_version":"1.0","source":{"id":"2604.10245","kind":"arxiv","version":2}},"canonical_sha256":"c6288302c3e8e8272dda730bc4ac2114de83365a693a9db42a4f48310bbb6515","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c6288302c3e8e8272dda730bc4ac2114de83365a693a9db42a4f48310bbb6515","first_computed_at":"2026-05-21T01:05:18.811077Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-21T01:05:18.811077Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"y8v8fu7ilNQag/cexqMeN4h6cBJ5+MNMgsKLI6P/pgxWBe9eugMLZG1Mz/Fa5ytybGnBKSC+nDLSPKpmhTsbAw==","signature_status":"signed_v1","signed_at":"2026-05-21T01:05:18.811565Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.10245","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:96de47625817ada76cd0b5a9fb05bbb544f8a88c3a977a19410daa2ea7221e84","sha256:e64ef4682c0603b0386b989956a665d76308a0e048fc5f217022e5ec95272c27"],"state_sha256":"54bbe353bca04399cbcb787b39d4950664b065cc3cbbe16e5b07bb7c49b55582"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HiN50CdI8e1qoRPHvSbNh2/x+S0wMjKJtuyxTHbjucLv2oWt3Vk6twgApGEtCAQBVMW+ziRg0tmZCgYAChc2Bg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T20:35:08.252922Z","bundle_sha256":"c90c63690f5609187631631c4cf1381541a3580935a129c12155dab0c1acab25"}}