{"id":"W4417399411","doi":"10.1016/j.neuroimage.2025.121656","title":"Spatially regularized super-resolved constrained spherical deconvolution (SR2-CSD) of diffusion MRI data","year":2025,"lang":"en","type":"article","venue":"NeuroImage","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Social Fund; Ministerio de Ciencia, Innovación y Universidades","keywords":"Deconvolution; Spatial coherence; Prior probability; Tractography; Image resolution; Spherical harmonics; Diffusion MRI; Orientation (vector space); Coherence (philosophical gambling strategy)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002386611,0.001087729,0.000713197,0.001061863,0.0003989167,0.0009217053,0.001027223,0.0008697805,0.001339166],"category_scores_gemma":[0.008129852,0.0003869123,0.0008132391,0.0009939664,0.0008164085,0.001169789,0.001338901,0.001184449,0.0006026013],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000536597,"about_ca_system_score_gemma":0.002221658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004600837,"about_ca_topic_score_gemma":0.007791411,"domain_scores_codex":[0.9991639,0.0001966004,0.00006568404,0.0001705906,0.0003545707,0.00004877752],"domain_scores_gemma":[0.9977963,0.0008266753,0.0002979892,0.0004155827,0.0005805561,0.00008296329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000992281,0.0002168071,0.005148083,0.001360969,0.0005934475,0.0005659951,0.0005768056,0.3350675,0.2533291,0.0162793,0.01395562,0.3719142],"study_design_scores_gemma":[0.00006472033,0.0001847076,0.003446899,0.00007405956,0.0001062037,0.000761544,0.00009596568,0.8591259,0.1120283,0.009495192,0.01445376,0.0001627674],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04314167,0.0008437293,0.9508292,0.0003232117,0.00007026664,0.0001057243,0.0007897989,0.002625714,0.001270696],"genre_scores_gemma":[0.2170228,0.001016647,0.7761093,0.0003431824,0.00004687083,0.0002551189,0.002489709,0.0009795087,0.00173697],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004600837,"threshold_uncertainty_score":0.01262176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0675359183058252,"score_gpt":0.3609758912461194,"score_spread":0.2934399729402942,"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."}}