{"id":"W4281698926","doi":"10.1038/s41592-022-01458-7","title":"ASLPrep: a platform for processing of arterial spin labeled MRI and quantification of regional brain perfusion","year":2022,"lang":"en","type":"article","venue":"Nature Methods","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; University of British Columbia","funders":"Eurostars; National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institute on Aging; EU Joint Programme – Neurodegenerative Disease Research; Alzheimer Nederland; U.S. Department of Health and Human Services; National Institutes of Health; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Commission; University of Pennsylvania; Rijksdienst voor Ondernemend Nederland; National Science Foundation","keywords":"Arterial spin labeling; Magnetic resonance imaging; Suite; Software; Perfusion; Software suite; Nuclear magnetic resonance; Biomedical engineering; Medicine; Computer science; Radiology; Physics","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":[0.001855516,0.001594503,0.001000275,0.00144567,0.001063691,0.002359189,0.003021175,0.002205091,0.02072193],"category_scores_gemma":[0.002283863,0.001406289,0.0008602639,0.0009022128,0.0006696357,0.001573368,0.001741814,0.002265298,0.008621753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004830281,"about_ca_system_score_gemma":0.001218775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008641726,"about_ca_topic_score_gemma":0.002079162,"domain_scores_codex":[0.9992245,0.00009289524,0.00004744978,0.000163961,0.0003996569,0.00007155273],"domain_scores_gemma":[0.9989446,0.0003030281,0.0001507564,0.0002033954,0.0002729484,0.0001252705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001511425,0.0002306315,0.0009505841,0.001218723,0.0003103146,0.0006970163,0.0003849771,0.002977251,0.7317481,0.007968022,0.07756683,0.1744361],"study_design_scores_gemma":[0.0002569483,0.0004269106,0.003225745,0.0001157372,0.0002099752,0.001763184,0.00008447273,0.07200563,0.7742229,0.008582059,0.1388399,0.0002666908],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.0276878,0.001683094,0.8581546,0.0006261264,0.0006350598,0.0005335086,0.008225475,0.09379321,0.008661099],"genre_scores_gemma":[0.05539484,0.001410169,0.900965,0.0007958224,0.0003027878,0.002266464,0.009432365,0.01468889,0.01474363],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02072193,"threshold_uncertainty_score":0.06932181,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05046838000190946,"score_gpt":0.4605283796501679,"score_spread":0.4100599996482584,"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."}}