{"id":"W2166847927","doi":"10.1109/42.875186","title":"An inverse problem approach to the correction of distortion in EPI images","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Imaging phantom; Distortion (music); Encoding (memory); Inverse problem; Conjugate gradient method; Echo-planar imaging; Iterative reconstruction; Planar; Algorithm; Mathematics; Computer science; Physics; Artificial intelligence; Magnetic resonance imaging; Mathematical analysis; Optics","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":[0.0007089657,0.001008344,0.0006646857,0.0004982888,0.0003546557,0.0008547568,0.001177453,0.00140787,0.002948039],"category_scores_gemma":[0.002579284,0.0003905316,0.0007185613,0.0004839916,0.0007095294,0.001195746,0.000868419,0.001653816,0.001111387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001989423,"about_ca_system_score_gemma":0.0008110126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008409703,"about_ca_topic_score_gemma":0.001226099,"domain_scores_codex":[0.9995746,0.0001128268,0.00002464404,0.00007864395,0.0001912909,0.00001803007],"domain_scores_gemma":[0.9995115,0.0002358327,0.00004123259,0.00007468666,0.0001208169,0.00001601199],"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":[0.0001299366,0.0001647361,0.0004898578,0.0005651601,0.0002226371,0.0004859375,0.0003095679,0.2634241,0.09464084,0.1199502,0.005563711,0.5140533],"study_design_scores_gemma":[0.0000364966,0.0001509602,0.0004415682,0.00002193084,0.00004348573,0.0008818,0.00004126395,0.921286,0.02899152,0.0277844,0.02026233,0.00005809158],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007369153,0.00008440914,0.9984719,0.00009968162,0.00003456564,0.00001554979,0.00001021678,0.00009519954,0.0004515367],"genre_scores_gemma":[0.01763659,0.0003967628,0.9779457,0.00008931016,0.0001074015,0.00007447299,0.00005340068,0.00009631061,0.003600074],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002948039,"threshold_uncertainty_score":0.009862185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01195125924177867,"score_gpt":0.3031353823266978,"score_spread":0.2911841230849191,"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."}}