{"id":"W2081483096","doi":"10.1002/mrm.21312","title":"Time‐resolved MR angiography with limited projections","year":2007,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"","keywords":"Computer science; Contrast (vision); SIGNAL (programming language); Angiography; Artificial intelligence; Gadolinium; Magnetic resonance angiography; Computer vision; Nuclear medicine; Magnetic resonance imaging; Radiology; Materials science; Medicine","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":[0.001509158,0.0005997813,0.0007274397,0.001007639,0.0003164912,0.001014169,0.001010855,0.0008292983,0.003213474],"category_scores_gemma":[0.002531173,0.0008575092,0.0005827786,0.0007593879,0.0004696742,0.00081299,0.001135049,0.001194215,0.001198712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002502359,"about_ca_system_score_gemma":0.0008103198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005852235,"about_ca_topic_score_gemma":0.0009913425,"domain_scores_codex":[0.9992428,0.0003274816,0.00003886369,0.00008845743,0.000265232,0.00003713138],"domain_scores_gemma":[0.9989535,0.0004044735,0.0001013261,0.0003125511,0.0001676824,0.0000604593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004613913,0.0001348285,0.002007202,0.0004641895,0.0002212989,0.0005163429,0.0001925836,0.05675828,0.2695789,0.03966857,0.004749943,0.6252466],"study_design_scores_gemma":[0.00007993141,0.0002029652,0.002024787,0.00005867454,0.00005190751,0.003342554,0.00002407068,0.87853,0.08585061,0.008710175,0.02101996,0.0001043807],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002393223,0.0001178979,0.9965726,0.00003951507,0.000008877373,0.00003822393,0.00002524851,0.0003966714,0.0004076112],"genre_scores_gemma":[0.01905927,0.000200324,0.9798553,0.00002545071,0.00001056023,0.000107801,0.00006313492,0.00008190971,0.0005961852],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003213474,"threshold_uncertainty_score":0.01075011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01521966525165887,"score_gpt":0.3010346195723506,"score_spread":0.2858149543206917,"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."}}