{"id":"W4317933825","doi":"10.1002/mrm.29588","title":"Multi‐echo dipole inversion for magnetic susceptibility mapping","year":2023,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of British Columbia Hospital","funders":"Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Multiple Sclerosis Society of Canada","keywords":"Quantitative susceptibility mapping; Computer science; Inversion (geology); Algorithm; Inverse problem; Source field; Echo (communications protocol); Artificial intelligence; Physics; Mathematics; Magnetic resonance imaging; Geology; Optics; Near and far field; Mathematical analysis; Radiology","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":[0.0009806831,0.0009108815,0.0003648505,0.0006820172,0.0002317739,0.0005706808,0.0008235347,0.0008984755,0.002672922],"category_scores_gemma":[0.00420081,0.000289435,0.0006278157,0.0005647094,0.0003581603,0.0006908199,0.0009540042,0.001004274,0.001955679],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004074948,"about_ca_system_score_gemma":0.001066666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001890066,"about_ca_topic_score_gemma":0.002707689,"domain_scores_codex":[0.9996706,0.0001182323,0.00001485053,0.00006541085,0.0001133507,0.00001748949],"domain_scores_gemma":[0.9990736,0.0003950319,0.0001149871,0.0001622922,0.0002101173,0.00004402789],"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":[0.0004777472,0.0001445643,0.001866495,0.0008126955,0.0002469585,0.0002900426,0.0001364355,0.2903164,0.08465274,0.01126607,0.01589389,0.593896],"study_design_scores_gemma":[0.0000534576,0.0001278034,0.001453797,0.00006597407,0.0000515432,0.0004824974,0.00003041159,0.9275673,0.03515293,0.01742434,0.01754778,0.00004221048],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008909209,0.001075777,0.9866144,0.0003808312,0.00008216502,0.00005772977,0.0003299713,0.001574783,0.0009750989],"genre_scores_gemma":[0.2394331,0.001566316,0.7513511,0.0003194186,0.0001283372,0.0002285863,0.001887842,0.0005790574,0.004506221],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002672922,"threshold_uncertainty_score":0.00894177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05404748669174236,"score_gpt":0.3535730913842277,"score_spread":0.2995256046924853,"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."}}