{"id":"W4387139287","doi":"10.1101/2023.09.26.559530","title":"Influence of preprocessing, distortion correction and cardiac triggering on the quality of diffusion MR images of spinal cord","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute; Centre Hospitalier Universitaire Sainte-Justine; Université de Montréal; Université de Sherbrooke","funders":"National Institutes of Health","keywords":"Distortion (music); Image quality; Preprocessor; Artificial intelligence; Diffusion MRI; Computer vision; Spinal cord; Computer science; Tractography; White matter; Image processing; Contrast (vision); Pattern recognition (psychology); Image (mathematics); Medicine; Magnetic resonance imaging; Radiology; Neuroscience; Psychology","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":[0.002139861,0.0009059798,0.0005588729,0.0006403655,0.0004509251,0.001044526,0.0005345048,0.0007028359,0.0009211176],"category_scores_gemma":[0.01616749,0.0002689376,0.0004936334,0.0006106377,0.0005176855,0.0005823539,0.0004736193,0.0004563124,0.0002376742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002880036,"about_ca_system_score_gemma":0.0005276039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001989908,"about_ca_topic_score_gemma":0.002224872,"domain_scores_codex":[0.9989035,0.0004071534,0.0001647323,0.0001978574,0.0002332233,0.00009354377],"domain_scores_gemma":[0.9911708,0.005623614,0.001223107,0.000712448,0.00104468,0.00022542],"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":[0.00842064,0.0006849236,0.02998413,0.001618359,0.0008112373,0.000671938,0.0003624678,0.04502467,0.8020902,0.0004085522,0.001021945,0.1089008],"study_design_scores_gemma":[0.0004164798,0.01032789,0.1363831,0.0003047888,0.001373514,0.001580231,0.0003581491,0.118109,0.7261491,0.0008221741,0.003951318,0.0002242916],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9604951,0.002058586,0.03550543,0.0001963802,0.0001514707,0.0001591543,0.0003505165,0.0005290426,0.0005543407],"genre_scores_gemma":[0.9473608,0.0008121397,0.04982739,0.000189638,0.00004969117,0.0001074557,0.0008641679,0.0003142247,0.0004744939],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002139861,"threshold_uncertainty_score":0.01131684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05972007701938037,"score_gpt":0.3414014745011572,"score_spread":0.2816813974817768,"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."}}