{"id":"W3127672653","doi":"10.1002/mrm.28691","title":"Characterization and compensation of inhomogeneity artifact in spiral hyperpolarized<sup>13</sup>C imaging of the human heart","year":2021,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"General Electric (Canada)","funders":"National Center for Research Resources; National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Image quality; Spiral (railway); Full width at half maximum; Waveform; Artifact (error); Nuclear medicine; SIGNAL (programming language); Physics; Nuclear magnetic resonance; Optics; Biomedical engineering; Mathematics; Medicine; Computer science; Image (mathematics); Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001498075,0.00008689422,0.0002341003,0.00004135574,0.00003237592,0.000003221185,0.00009489843,0.0000438336,0.0001722815],"category_scores_gemma":[0.0000993705,0.00007138351,0.00002051164,0.0002709356,0.0002007216,0.00004420334,0.00005838442,0.0001477011,2.389909e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002522148,"about_ca_system_score_gemma":0.00002188282,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002021613,"about_ca_topic_score_gemma":0.00006986278,"domain_scores_codex":[0.9991044,0.00003131183,0.0004076838,0.0001862724,0.000156897,0.000113458],"domain_scores_gemma":[0.9994712,0.00004588397,0.0001149773,0.0002902379,0.00005723223,0.00002044029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000005146455,0.00003564709,0.3358434,0.00005260918,3.810624e-7,0.000001410718,0.0002007642,0.0000151551,0.6487066,0.0002277399,0.000005247566,0.01490598],"study_design_scores_gemma":[0.0007390855,0.00002215613,0.8311897,0.000497854,0.000007911224,0.0000114088,0.000203516,0.00406807,0.1591256,0.0009035,0.003153042,0.00007813567],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9970362,0.001146615,0.0002128113,0.001019333,0.000005331095,0.0001181244,0.00001074518,0.00001053212,0.0004402932],"genre_scores_gemma":[0.9987781,0.0001579653,0.0007033233,0.000121628,0.00003211335,0.00002417146,0.00003271251,0.000008739593,0.0001412554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4953463,"threshold_uncertainty_score":0.2910935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01608490784617911,"score_gpt":0.2802873057600969,"score_spread":0.2642023979139178,"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."}}