{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000262017,0.0001292737,0.0003232505,0.0002510608,0.00007651958,0.00003029715,0.00007720192,0.00005938652,0.0003981941],"category_scores_gemma":[0.0003114541,0.00009283706,0.0001649649,0.001521182,0.0001583594,0.0002090296,0.00003408288,0.0003011869,0.00001383691],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005967873,"about_ca_system_score_gemma":0.0001163326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004534981,"about_ca_topic_score_gemma":0.000008185161,"domain_scores_codex":[0.9986051,0.00005007187,0.0003384789,0.0002810381,0.000589799,0.0001355622],"domain_scores_gemma":[0.9990588,0.000276442,0.0001524491,0.0002280697,0.0001713681,0.0001128533],"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":[0.002297287,0.003843474,0.09944618,0.005070638,0.008277735,0.0007792359,0.008490799,0.009423039,0.3487405,0.04162788,0.01101489,0.4609884],"study_design_scores_gemma":[0.001014949,0.0007326915,0.008752499,0.0008833053,0.001685109,0.00002547216,0.0001802549,0.9654976,0.0150574,0.001197066,0.004735288,0.0002383712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02084418,0.00004600942,0.969558,0.006451109,0.000005530361,0.002225146,0.00001248319,0.0002700838,0.0005874721],"genre_scores_gemma":[0.8543247,0.0001211976,0.1403509,0.0006022421,0.0001021932,0.002701634,0.0009697606,0.00004501998,0.0007823676],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9560745,"threshold_uncertainty_score":0.4359946,"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."}}