{"id":"W2028491648","doi":"10.1002/mrm.20145","title":"Accelerating cardiac cine 3D imaging using <i>k‐t</i> BLAST","year":2004,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eurostars; Canadian Institutes of Health Research; Kommission für Technologie und Innovation","keywords":"Nuclear medicine; Cardiac imaging; Nuclear magnetic resonance; Computer science; Medicine; Physics; Radiology","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":[0.0005978622,0.000426203,0.0002922743,0.0003880208,0.0001786131,0.0004812778,0.0004076075,0.0004436923,0.003159546],"category_scores_gemma":[0.001605166,0.0002879408,0.0002044942,0.0004086921,0.0003767559,0.0003833126,0.0005510981,0.000495578,0.001052444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001707387,"about_ca_system_score_gemma":0.0003428965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008067667,"about_ca_topic_score_gemma":0.001273339,"domain_scores_codex":[0.9998848,0.00002697389,0.00001128848,0.00001968165,0.00004334774,0.00001382935],"domain_scores_gemma":[0.9995388,0.0002152392,0.00006409791,0.00007169505,0.00008363165,0.00002656839],"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":[0.0006196623,0.00005454726,0.001261498,0.000414011,0.00003931943,0.0005017375,0.0003388136,0.01162784,0.8552764,0.003732251,0.003436901,0.122697],"study_design_scores_gemma":[0.0002211619,0.0007825915,0.01145071,0.0001174163,0.0001058346,0.005926626,0.000109086,0.2199674,0.7175102,0.004273644,0.03940026,0.0001350784],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07612474,0.0003336341,0.9152183,0.000190847,0.00004405913,0.0001581513,0.000208336,0.004101848,0.003620101],"genre_scores_gemma":[0.137517,0.0003514218,0.8595195,0.00007471053,0.00001628584,0.0001417888,0.0002447763,0.0003991687,0.001735304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003159546,"threshold_uncertainty_score":0.01056975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02900100400978175,"score_gpt":0.331238861883126,"score_spread":0.3022378578733443,"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."}}