{"id":"W4390603730","doi":"10.1002/mrm.29988","title":"An augmented hybrid multibaseline and referenceless MR thermometry motion compensation algorithm for MRgHIFU hyperthermia","year":2024,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Ultrasound and Hyperthermia Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation","keywords":"Motion compensation; Atlas (anatomy); Computer science; Artificial intelligence; Motion (physics); Algorithm; Principal component analysis; Computer vision; Biomedical engineering; Physics; Nuclear medicine; Medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005492131,0.0006756545,0.0003906247,0.0004802068,0.0002660742,0.0005636646,0.0005864218,0.0004162734,0.001781793],"category_scores_gemma":[0.001159051,0.0003205683,0.0003842093,0.0004597946,0.000226994,0.0005310506,0.0004864476,0.0005965121,0.0006734449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00039801,"about_ca_system_score_gemma":0.0007448845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002375236,"about_ca_topic_score_gemma":0.003914067,"domain_scores_codex":[0.9996916,0.00006017465,0.00001598946,0.0000741335,0.0001239912,0.00003423577],"domain_scores_gemma":[0.9997187,0.0000508847,0.00004372469,0.00005057975,0.0001193127,0.00001677658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004498348,0.00007492689,0.001473791,0.0001238432,0.00007573972,0.00008343599,0.0001294868,0.1163351,0.1309783,0.002050383,0.003358947,0.7448661],"study_design_scores_gemma":[0.00002715468,0.0001770133,0.00368957,0.0000132946,0.00004962263,0.0002931595,0.00002134469,0.9162285,0.07128994,0.0008505688,0.007310357,0.00004950649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01562026,0.0001699826,0.9818665,0.00007483563,0.00003326656,0.00003765018,0.00005874661,0.001488838,0.0006499654],"genre_scores_gemma":[0.2822671,0.0001781072,0.7133365,0.0001055084,0.00003608392,0.0001129209,0.0002497766,0.0003632297,0.003350864],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002375236,"threshold_uncertainty_score":0.005960703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602831738301375,"score_gpt":0.2637600398482303,"score_spread":0.2477317224652165,"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."}}