{"id":"W1984493340","doi":"10.1016/j.jmr.2005.07.006","title":"Acquisition time reduction in magnetic resonance spectroscopic imaging using discrete wavelet encoding","year":2005,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"","keywords":"Wavelet; Discrete wavelet transform; Haar wavelet; Magnetic resonance spectroscopic imaging; Encoding (memory); Wavelet transform; Computer science; Nuclear magnetic resonance; Artificial intelligence; Physics; Mathematics; Magnetic resonance imaging","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":[0.00055698,0.0003896605,0.0003459698,0.0003562844,0.0001730494,0.000527107,0.0004114639,0.0004736897,0.0009433217],"category_scores_gemma":[0.00218178,0.0002529252,0.0003025531,0.0005062881,0.0002876266,0.0008848146,0.0004787995,0.000817141,0.0002792429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000207419,"about_ca_system_score_gemma":0.0003883942,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006803128,"about_ca_topic_score_gemma":0.0008882302,"domain_scores_codex":[0.9998074,0.0000474818,0.00001448649,0.00002185987,0.00009331901,0.00001540725],"domain_scores_gemma":[0.9992113,0.0004456736,0.00006846447,0.00009598045,0.0001491707,0.0000294439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008644286,0.0002114396,0.0009466452,0.000290129,0.00005998639,0.0001444869,0.0002290015,0.06855384,0.3224004,0.02078431,0.001482012,0.5840334],"study_design_scores_gemma":[0.00002975653,0.0001466661,0.0012465,0.00001711762,0.00005335798,0.0002678024,0.0000373953,0.8534034,0.1343674,0.006310408,0.004097136,0.00002299692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03617502,0.0002964688,0.962712,0.000186129,0.00004077864,0.00001511043,0.00003028599,0.0001246324,0.0004194939],"genre_scores_gemma":[0.2119786,0.0008978675,0.7835395,0.00007915644,0.00008263253,0.0000565251,0.0001562991,0.0001789739,0.00303055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009433217,"threshold_uncertainty_score":0.003155768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01242146753755141,"score_gpt":0.2725625566735541,"score_spread":0.2601410891360026,"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."}}