{"id":"W4394709965","doi":"10.1088/1361-6560/ad3db8","title":"Image denoising and model-independent parameterization for IVIM MRI","year":2024,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Foundation; BC Cancer Agency; Surrey Memorial Hospital; University of British Columbia","funders":"","keywords":"Intravoxel incoherent motion; Image quality; Deconvolution; Mathematics; Noise reduction; Artificial intelligence; Diffusion MRI; Pattern recognition (psychology); Nuclear medicine; Magnetic resonance imaging; Computer science; Statistics; Image (mathematics); Medicine; Radiology","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":[0.00218097,0.0008939101,0.0005374943,0.0008467949,0.0001912675,0.000682417,0.0007147122,0.0007996411,0.0007120696],"category_scores_gemma":[0.007047109,0.0004232843,0.0006821362,0.0004929888,0.0005205579,0.0008383544,0.0009236658,0.001073921,0.0003642486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005882333,"about_ca_system_score_gemma":0.0006234406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001043055,"about_ca_topic_score_gemma":0.001288102,"domain_scores_codex":[0.9994652,0.0001895208,0.00003581378,0.0001046815,0.0001726311,0.00003211631],"domain_scores_gemma":[0.998763,0.0004589413,0.0002632514,0.0001863022,0.0002964996,0.00003205104],"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":[0.0004680459,0.0001801604,0.004824298,0.0005488319,0.0002385832,0.0001356342,0.0002806063,0.2053824,0.4554571,0.004723067,0.001494501,0.3262669],"study_design_scores_gemma":[0.00002419387,0.0002315401,0.003791966,0.00004177401,0.00008865217,0.0002663496,0.00003968084,0.865766,0.1238579,0.002965548,0.002875103,0.0000513286],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02932213,0.0002580011,0.9695733,0.0001119818,0.00001929123,0.00003657908,0.00005018943,0.0003819157,0.0002465505],"genre_scores_gemma":[0.2699734,0.0004228382,0.7274922,0.0001268221,0.00004314434,0.0001993468,0.0002600741,0.0004498531,0.001032341],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00218097,"threshold_uncertainty_score":0.01153415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1896142740097208,"score_gpt":0.4602675640133927,"score_spread":0.2706532900036719,"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."}}