{"id":"W4221119639","doi":"10.1002/jmri.28180","title":"Impact of Temporal Resolution and Methods for Correction on Cardiac Magnetic Resonance Perfusion Quantification","year":2022,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; EPSRC Centre for Doctoral Training in Medical Imaging; King's College London; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research; Centre For Medical Engineering, King’s College London; Medical Research Council Canada; British Heart Foundation; Wellcome Trust","keywords":"Imaging phantom; Deconvolution; Perfusion; Temporal resolution; Magnetic resonance imaging; Nuclear medicine; Biomedical engineering; Computer science; Medicine; Algorithm; Cardiology; 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":[0.009771992,0.0009881472,0.0004843211,0.0006711198,0.0004768199,0.001081817,0.0007960743,0.0009168359,0.001234138],"category_scores_gemma":[0.04143714,0.0004899395,0.0007457272,0.0009126133,0.0005236364,0.0008430451,0.0009536149,0.001062316,0.0002970655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008380133,"about_ca_system_score_gemma":0.001177823,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001877712,"about_ca_topic_score_gemma":0.001665853,"domain_scores_codex":[0.9955164,0.00192238,0.0003579039,0.0006285127,0.001459131,0.0001156796],"domain_scores_gemma":[0.9669269,0.02609919,0.002088201,0.002441456,0.002253359,0.0001909204],"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":[0.004014391,0.0003532682,0.04065385,0.002296364,0.001505939,0.0008007005,0.001103685,0.1422396,0.3350977,0.01079782,0.002952411,0.4581843],"study_design_scores_gemma":[0.0001818902,0.001119048,0.06032696,0.0007046032,0.001056756,0.004032526,0.0002342699,0.4647979,0.4415961,0.00765011,0.01794263,0.0003572775],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1292674,0.005571366,0.8604261,0.0006629465,0.0002797714,0.0001720157,0.0003604737,0.001488124,0.001771836],"genre_scores_gemma":[0.4331866,0.001772442,0.5608408,0.0003393763,0.00008649734,0.0003365362,0.00054663,0.001685195,0.00120585],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009771992,"threshold_uncertainty_score":0.05167985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02206941347148123,"score_gpt":0.3847993347001483,"score_spread":0.3627299212286671,"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."}}