{"id":"W4392715329","doi":"10.1002/mrm.30083","title":"Inline automatic quality control of <scp>2D</scp> phase‐contrast flow <scp>MRI</scp> for subject‐specific scan time adaptation","year":2024,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Siemens (Canada)","funders":"National Heart, Lung, and Blood Institute; NHLBI Division of Intramural Research; National Institutes of Health","keywords":"Image quality; Segmentation; Aorta; Computer science; Contrast (vision); Spiral (railway); Population; Artificial intelligence; Nuclear medicine; Computer vision; Biomedical engineering; Medicine; Image (mathematics); Mathematics; Cardiology","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":[0.001773627,0.0008208386,0.0003579867,0.0004299763,0.0002698239,0.0008148658,0.0009795149,0.0006403878,0.002360412],"category_scores_gemma":[0.004591973,0.0004640186,0.0001934075,0.0002123311,0.0004451007,0.0005992956,0.0006770152,0.0005276735,0.0005996673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003260523,"about_ca_system_score_gemma":0.0004557655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000769645,"about_ca_topic_score_gemma":0.0011605,"domain_scores_codex":[0.999424,0.0001878355,0.00003863395,0.0001293109,0.0001856328,0.00003472584],"domain_scores_gemma":[0.9973595,0.0009113907,0.0005875849,0.0003507228,0.0006661733,0.0001246805],"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":[0.001037764,0.0002147206,0.004773729,0.0004401841,0.00007033609,0.0002432686,0.00042382,0.004037556,0.8647117,0.0004338597,0.003874598,0.1197386],"study_design_scores_gemma":[0.0001973964,0.001278369,0.02818569,0.00006741434,0.0001552321,0.001882629,0.00008306755,0.1335901,0.8183348,0.0006954603,0.01540808,0.0001218593],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1372675,0.0004612361,0.8529051,0.0003136728,0.00009774501,0.0002854294,0.000272621,0.007171294,0.001225446],"genre_scores_gemma":[0.5950015,0.0002782794,0.3987514,0.0005979714,0.0001669751,0.0004955341,0.0005812748,0.002253277,0.001873775],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002360412,"threshold_uncertainty_score":0.009379923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02756192393655601,"score_gpt":0.3420208256603363,"score_spread":0.3144589017237803,"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."}}