{"id":"W4400305759","doi":"10.1016/j.acra.2024.06.029","title":"Improved Diffusion-Weighted Hyperpolarized 129Xe Lung MRI with Patch-Based Higher-Order, Singular Value Decomposition Denoising","year":2024,"lang":"en","type":"article","venue":"Academic Radiology","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Heart, Lung, and Blood Institute; Cystic Fibrosis Foundation; University of California, San Francisco; McMaster University; National Institutes of Health","keywords":"Xenon; Diffusion MRI; Singular value decomposition; Nuclear magnetic resonance; Diffusion; Noise reduction; Decomposition; Medicine; Mathematics; Chemistry; Physics; Magnetic resonance imaging; Radiology; Algorithm; Acoustics; Nuclear physics; Thermodynamics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.0007840325,0.0007446202,0.0009885955,0.0005024431,0.0001697582,0.0006972649,0.0006577587,0.001092197,0.001220287],"category_scores_gemma":[0.00140784,0.0003709969,0.000793784,0.0005664414,0.0003305798,0.0007859198,0.0007295705,0.001021097,0.0008306139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001833358,"about_ca_system_score_gemma":0.0005982709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001527486,"about_ca_topic_score_gemma":0.002637404,"domain_scores_codex":[0.9997426,0.00005541664,0.00001567544,0.00005738475,0.0001026008,0.00002622154],"domain_scores_gemma":[0.9995871,0.0001202815,0.00004356182,0.00007090452,0.0001432305,0.00003482411],"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":[0.0008531995,0.0002991192,0.001848976,0.0004725092,0.0003242778,0.0004087099,0.0001796508,0.1514992,0.4175236,0.005162874,0.004886027,0.4165419],"study_design_scores_gemma":[0.00001609991,0.0000752041,0.0009065648,0.00001378579,0.00006045627,0.0002645645,0.00001816957,0.9658003,0.02972523,0.001193287,0.001906917,0.00001940374],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02464494,0.0004383476,0.9731919,0.0001820493,0.00005694008,0.00002291916,0.00009732854,0.0004678663,0.0008976345],"genre_scores_gemma":[0.2062438,0.0008425971,0.7864411,0.0002038041,0.0001053408,0.00005421376,0.0007370748,0.0003595996,0.005012538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001527486,"threshold_uncertainty_score":0.004146457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008318379463659806,"score_gpt":0.2894846194782236,"score_spread":0.2811662400145638,"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."}}