{"id":"W2129580882","doi":"10.1109/tns.2004.829786","title":"Sinogram-based motion correction of PET images using optical motion tracking system and list-mode data acquisition","year":2004,"lang":"en","type":"article","venue":"IEEE Transactions on Nuclear Science","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Addiction and Mental Health","funders":"","keywords":"Computer vision; Artifact (error); Match moving; Artificial intelligence; Tracking (education); Motion (physics); Data acquisition; Positron emission tomography; Computer science; Motion compensation; Iterative reconstruction; Tracking system; Physics; Nuclear medicine; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005177033,0.0001072388,0.0001602366,0.0002310447,0.0003519529,0.00006368057,0.0001698322,0.00004919056,0.00001811819],"category_scores_gemma":[0.00003077725,0.0001005655,0.00004155274,0.0005576484,0.0005619526,0.0004332481,0.000004521569,0.0002044616,0.000004723841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002163964,"about_ca_system_score_gemma":0.00009770918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001942809,"about_ca_topic_score_gemma":0.000003612261,"domain_scores_codex":[0.9987292,0.00002458167,0.0002414918,0.0004028212,0.000424421,0.0001775129],"domain_scores_gemma":[0.999084,0.00003705699,0.00008401903,0.0004888345,0.0001568607,0.0001492135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001300271,0.0007884495,0.00003235629,0.0002504624,0.00001197465,0.00001311236,0.0001330249,0.02255227,0.9077544,0.0003191993,0.00004068195,0.06797405],"study_design_scores_gemma":[0.0005158453,0.0001448318,0.0007996986,0.0005173383,0.0001049351,0.0002055888,0.0001403603,0.8829234,0.1145148,0.00001515538,0.00001801537,0.00009997499],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3782472,0.000003193513,0.6205901,0.0005625688,0.0001538559,0.0002111574,0.00001706658,0.0001557129,0.0000591612],"genre_scores_gemma":[0.9321468,0.000009685331,0.06770012,0.00008604668,0.00002827182,0.000005215407,0.000004248205,0.00001447482,0.00000512658],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8603711,"threshold_uncertainty_score":0.4100943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04147098352767358,"score_gpt":0.332864655229373,"score_spread":0.2913936717016994,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}