{"id":"W1969506215","doi":"10.1002/mrm.21842","title":"Design of cosine modulated very selective suppression pulses for MR spectroscopic imaging at 3T","year":2008,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"National Institute of General Medical Sciences; National Cancer Institute; National Institutes of Health","keywords":"Magnetic resonance spectroscopic imaging; Scanner; Pulse sequence; Pulse (music); Nuclear magnetic resonance; Image quality; Nuclear medicine; Biomedical engineering; Materials science; Computer science; Magnetic resonance imaging; Optics; Medicine; Physics; Radiology; Artificial intelligence; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008266416,0.0004962504,0.0003799997,0.0002833161,0.0002577755,0.0005299741,0.0009052624,0.0007061495,0.00132797],"category_scores_gemma":[0.001356099,0.0004496246,0.0001922055,0.0003251468,0.0003187547,0.0003915969,0.00024684,0.000538761,0.0005266286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005680513,"about_ca_system_score_gemma":0.0007906081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006985026,"about_ca_topic_score_gemma":0.001044075,"domain_scores_codex":[0.9997602,0.00005055113,0.00001853304,0.00005934385,0.00008406588,0.000027313],"domain_scores_gemma":[0.9993344,0.0002097334,0.0001270125,0.00004589374,0.0002109068,0.00007207496],"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":[0.0007347415,0.0001514647,0.0008284901,0.0002488281,0.00002782378,0.0002049681,0.0001423021,0.008159957,0.9438904,0.002186122,0.0005772447,0.04284761],"study_design_scores_gemma":[0.0002989283,0.001762855,0.003319798,0.00005134948,0.0001048673,0.0007290876,0.00003911308,0.1095374,0.8653867,0.001013882,0.01766407,0.00009182625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.244232,0.0008090963,0.7482438,0.0003655111,0.0001755025,0.001272305,0.0001987149,0.001420867,0.003282151],"genre_scores_gemma":[0.4092746,0.0006211165,0.5857744,0.000369256,0.00006115179,0.001730236,0.0001623336,0.0002602027,0.001746701],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00132797,"threshold_uncertainty_score":0.004442453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02754089733411367,"score_gpt":0.3204816378526142,"score_spread":0.2929407405185005,"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."}}