{"id":"W4392455053","doi":"10.1016/j.media.2024.103134","title":"Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Google (Canada); Université de Sherbrooke","funders":"Engineering and Physical Sciences Research Council; European Commission; Agencia Estatal de Investigación; Ministerio de Ciencia, Innovación y Universidades; Ministerio de Ciencia e Innovación; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Wellcome Trust","keywords":"Diffusion MRI; Protocol (science); Computer science; Artificial intelligence; Relaxation (psychology); Machine learning; Diffusion; Algorithm; Magnetic resonance imaging; Physics; Medicine","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":[0.004399549,0.001729677,0.0009833351,0.0009466044,0.0004200786,0.0009198266,0.001375184,0.001247767,0.0008377395],"category_scores_gemma":[0.01141748,0.0008590692,0.0009651828,0.000643627,0.0008125853,0.001583123,0.001355588,0.001813091,0.0004387006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008614685,"about_ca_system_score_gemma":0.002120714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00183376,"about_ca_topic_score_gemma":0.002504453,"domain_scores_codex":[0.9991441,0.0003678919,0.00006069125,0.0001938859,0.0001885312,0.00004489121],"domain_scores_gemma":[0.9972709,0.001650284,0.0003520387,0.0002651067,0.0003958658,0.00006586567],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003807612,0.0003046155,0.001598077,0.0004578322,0.0001856874,0.0001229391,0.000284988,0.7014962,0.07425004,0.004607703,0.0009763716,0.2153347],"study_design_scores_gemma":[0.00004653498,0.0002414607,0.0009186268,0.00002770612,0.00004569815,0.00009163355,0.00002177218,0.971235,0.01928505,0.006889238,0.001149479,0.00004779137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01787368,0.0001735734,0.980777,0.0001067573,0.00001281413,0.0001290277,0.00004335798,0.0005517372,0.000332148],"genre_scores_gemma":[0.1814097,0.0001957335,0.8166838,0.000113937,0.00001910405,0.0005866634,0.0002244573,0.000283221,0.0004833458],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004399549,"threshold_uncertainty_score":0.02326733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04037254488641366,"score_gpt":0.4036315344751152,"score_spread":0.3632589895887016,"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."}}