{"id":"W4313278763","doi":"10.1007/s10439-022-03117-6","title":"Design of a Patient-Specific Respiratory-Motion-Simulating Platform for In Vitro 4D Flow MRI","year":2022,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal; Siemens (Canada); Université de Montréal; Université du Québec; Centre Hospitalier de l’Université de Montréal","funders":"Canadian Institutes of Health Research; Shanghai Rising-Star Program; National Natural Science Foundation of China","keywords":"Imaging phantom; Magnetic resonance imaging; Gating; Biomedical engineering; Computer science; Artifact (error); Simulation; Signal-to-noise ratio (imaging); Acoustics; Computer vision; Physics; Engineering; Radiology; Medicine; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008114146,0.0008534173,0.000566658,0.0004597845,0.0003105706,0.0006632601,0.001440668,0.001050632,0.002172632],"category_scores_gemma":[0.0009136526,0.0006176329,0.0005293619,0.0001896326,0.0002959706,0.0004689902,0.0008955841,0.0004841644,0.001027888],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000311249,"about_ca_system_score_gemma":0.001348114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004793103,"about_ca_topic_score_gemma":0.0005850883,"domain_scores_codex":[0.9996864,0.000048721,0.00002772647,0.00007617274,0.0001163809,0.00004458857],"domain_scores_gemma":[0.9996076,0.0000955653,0.0000752003,0.00005539711,0.00008621577,0.00008002589],"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":[0.0002244107,0.0001874732,0.001347154,0.000445325,0.00004892378,0.0003988126,0.0001681575,0.01857659,0.9543325,0.00131535,0.0009884379,0.02196691],"study_design_scores_gemma":[0.0001841508,0.002353178,0.005425234,0.00008586414,0.0002653204,0.001403734,0.0001429932,0.1575301,0.7941833,0.00102446,0.03718797,0.0002137467],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1303425,0.0005676789,0.8616114,0.0003457288,0.0003177582,0.001442012,0.0006658431,0.002219678,0.002487357],"genre_scores_gemma":[0.3884232,0.0007894941,0.6032205,0.0003581034,0.00005073176,0.002470534,0.0007755619,0.0003105224,0.003601293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002172632,"threshold_uncertainty_score":0.007268131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05944317034335857,"score_gpt":0.3229468706402829,"score_spread":0.2635037002969244,"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."}}