{"id":"W4389037316","doi":"10.1002/mp.16844","title":"MRI motion artifact reduction using a conditional diffusion probabilistic model (MAR‐CDPM)","year":2023,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artifact (error); Artificial intelligence; Fluid-attenuated inversion recovery; Computer science; Magnetic resonance imaging; Diffusion MRI; Computer vision; Real-time MRI; Diffusion imaging; Pattern recognition (psychology); Nuclear medicine; Medicine; Radiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001324884,0.0001097699,0.0001669881,0.00004438952,0.0001354546,0.000007047829,0.00005728135,0.0001057121,0.00008149086],"category_scores_gemma":[0.0001046596,0.00009661997,0.00007442014,0.0003775914,0.0001365003,0.0000813665,0.00004941915,0.0001895391,0.00006785064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001140984,"about_ca_system_score_gemma":0.0001255547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007140301,"about_ca_topic_score_gemma":4.342128e-7,"domain_scores_codex":[0.9988015,0.00001258577,0.0002125779,0.0002520716,0.0005233448,0.0001979364],"domain_scores_gemma":[0.9994227,0.00003111211,0.00006393666,0.0002131725,0.00008769098,0.0001813482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003045348,0.004084304,0.001017005,0.0007348393,0.00009002351,0.0001075452,0.0008450617,0.2259106,0.2406045,0.1082868,0.03562563,0.3823892],"study_design_scores_gemma":[0.00035936,0.00004141475,0.0003596833,0.00009691693,0.00004288114,0.00003373272,0.00001800511,0.8049757,0.004442276,0.1891166,0.0004111005,0.0001023876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1233735,0.000005668724,0.8735632,0.001855916,0.00005675132,0.0003900954,0.00001579004,0.0003751983,0.0003639403],"genre_scores_gemma":[0.988479,0.00004956042,0.009657397,0.0002190102,0.0005499267,0.0001046971,0.0005620425,0.00002764342,0.0003506831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8651056,"threshold_uncertainty_score":0.3940048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05655673797675925,"score_gpt":0.3569183313510851,"score_spread":0.3003615933743259,"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."}}