{"id":"W4408126894","doi":"10.1002/mrm.30436","title":"<scp>3D MERMAID</scp> : <scp>3D</scp> Multi‐shot enhanced recovery motion artifact insensitive diffusion for submillimeter, multi‐shell, and <scp>SNR</scp> ‐efficient diffusion imaging","year":2025,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre; Montreal Neurological Institute and Hospital","funders":"Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada","keywords":"Imaging phantom; Flip angle; Physics; Pulse sequence; Artifact (error); Scanner; Optics; Single shot; Diffusion; Nuclear magnetic resonance; Materials science; Computer science; Computer vision; Magnetic resonance imaging","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003923676,0.0005304405,0.0002218879,0.0003540996,0.0002048875,0.0005198514,0.0006613318,0.0005786844,0.005859136],"category_scores_gemma":[0.0005413353,0.0002948306,0.0001764158,0.000264227,0.0003188775,0.0005427524,0.0004703364,0.0005264056,0.001420333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003983175,"about_ca_system_score_gemma":0.0006085809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001189842,"about_ca_topic_score_gemma":0.002622502,"domain_scores_codex":[0.999903,0.00001417817,0.000004312601,0.00001352024,0.00005685848,0.000008182723],"domain_scores_gemma":[0.9996527,0.00004234636,0.00005831567,0.00006181789,0.0001376995,0.0000470191],"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":[0.0007245259,0.0001304508,0.0009356178,0.0004079666,0.00007978663,0.001058017,0.0001169813,0.02691686,0.8494055,0.01409045,0.02791833,0.07821549],"study_design_scores_gemma":[0.000157868,0.0004651096,0.003607915,0.00005212337,0.00003417789,0.002357562,0.00003059506,0.2888416,0.6220073,0.002533514,0.07978436,0.0001279278],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2208676,0.001464848,0.7183105,0.001446743,0.0003744737,0.0004961276,0.002948234,0.01529015,0.03880141],"genre_scores_gemma":[0.4268711,0.0004206146,0.552177,0.0004073748,0.0000691105,0.0003959303,0.003038868,0.001612677,0.01500734],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005859136,"threshold_uncertainty_score":0.01960075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03692552816653036,"score_gpt":0.3238531735608782,"score_spread":0.2869276453943479,"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."}}