{"id":"W2125630043","doi":"10.1002/hbm.20453","title":"Optimization of the SNR‐resolution tradeoff for registration of magnetic resonance images","year":2007,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children; Toronto Centre for Phenogenomics; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; National Cancer Institute; National Institutes of Health; Canadian Institutes of Health Research; Hospital for Sick Children; Ontario Innovation Trust","keywords":"Magnetic resonance imaging; Resolution (logic); Image registration; Nuclear magnetic resonance; Functional magnetic resonance imaging; Computer vision; Artificial intelligence; Computer science; Physics; Image (mathematics); Psychology; Neuroscience; Medicine; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001178224,0.00006773388,0.0001003073,0.00009284725,0.0001194279,0.00002374543,0.0004192043,0.00004492563,0.0000158481],"category_scores_gemma":[0.0003280782,0.00005931633,0.00005431185,0.0002908848,0.0001316571,0.0002327511,0.00005250444,0.00005800506,2.100331e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003088381,"about_ca_system_score_gemma":0.00002797891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001437845,"about_ca_topic_score_gemma":0.000005257142,"domain_scores_codex":[0.9989238,0.0000632779,0.000418848,0.0001754461,0.0002812675,0.0001373758],"domain_scores_gemma":[0.9990519,0.000188504,0.0002926081,0.0003216613,0.0001205392,0.00002478492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001339211,0.0001070041,0.0002860304,0.0002634747,0.000006765027,0.000001118802,0.002296348,0.001169282,0.8396527,0.05639268,0.01400534,0.08580592],"study_design_scores_gemma":[0.002002534,0.000623439,0.1656203,0.0009035937,0.00002231965,0.00001336106,0.0004875925,0.201077,0.6006087,0.02438,0.003730253,0.0005308996],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001122529,0.0001650897,0.9965337,0.0008073634,0.00005922197,0.0004064553,0.000002240145,0.00006199547,0.000841384],"genre_scores_gemma":[0.2796139,0.000008384004,0.7192281,0.0004030048,0.00005707512,0.00002341886,0.000008299348,0.000009031577,0.000648698],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2784914,"threshold_uncertainty_score":0.241885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03105219102526123,"score_gpt":0.2901888968269667,"score_spread":0.2591367058017055,"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."}}