{"id":"W2146699192","doi":"10.1002/mrm.24845","title":"Prospective motion correction using inductively coupled wireless RF coils","year":2013,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; Association Canadienne des Technologues en Radiation Médicale; National Institute of Biomedical Imaging and Bioengineering; Lucas Foundation","keywords":"Wireless; Electromagnetic coil; Computer science; Match moving; Tracking (education); Radio frequency; Transmitter; Orientation (vector space); Scanner; Head (geology); SIGNAL (programming language); Position (finance); Computer vision; Artificial intelligence; Acoustics; Motion (physics); Physics; Telecommunications; Channel (broadcasting); Electrical engineering; Engineering; Mathematics; Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055999,0.0007921378,0.0003155494,0.0004144641,0.0001556435,0.0004008779,0.0007544614,0.0005087119,0.0008119658],"category_scores_gemma":[0.002954269,0.0003373683,0.0003301606,0.0003836745,0.0004567041,0.0006534418,0.0005529665,0.0004723255,0.0006391372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002429311,"about_ca_system_score_gemma":0.0003086245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002738007,"about_ca_topic_score_gemma":0.000405231,"domain_scores_codex":[0.9996082,0.00008608682,0.00002825796,0.00009177048,0.0001604992,0.00002518882],"domain_scores_gemma":[0.9990098,0.000290393,0.0003323671,0.0001579895,0.0001805085,0.00002883355],"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":[0.0004697377,0.00004302098,0.004141758,0.0004020087,0.00008535181,0.000542451,0.0002267925,0.003256849,0.7054146,0.001666359,0.001279231,0.2824719],"study_design_scores_gemma":[0.0001337903,0.001693675,0.01583101,0.00014319,0.0004254488,0.01203336,0.0000876901,0.03180492,0.8975713,0.001540809,0.03859649,0.0001384018],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05140816,0.001679687,0.9444444,0.0001552046,0.0001540911,0.00007391236,0.00004297612,0.001055599,0.0009858933],"genre_scores_gemma":[0.4695285,0.001862937,0.5242332,0.0003057813,0.0002167046,0.0001588973,0.0001687494,0.0002432908,0.003282008],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0008119658,"threshold_uncertainty_score":0.002961516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0248815115161684,"score_gpt":0.3143204948717577,"score_spread":0.2894389833555893,"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."}}