{"id":"W2984147745","doi":"10.1016/j.ejmp.2019.10.031","title":"Joint compensation of motion and partial volume effects by iterative deconvolution incorporating wavelet-based denoising in oncologic PET/CT imaging","year":2019,"lang":"en","type":"article","venue":"Physica Medica","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"Shahid Beheshti University of Medical Sciences; Tehran University of Medical Sciences and Health Services; University of Washington","keywords":"Deconvolution; Joint (building); Noise reduction; Wavelet; Partial volume; Volume (thermodynamics); Motion compensation; Computer science; Computer vision; Artificial intelligence; Nuclear medicine; Biomedical engineering; Medicine; Algorithm; Physics; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003958428,0.0001285332,0.000353665,0.00009201499,0.00004912904,0.00001228459,0.00005095552,0.00001504457,0.00001724661],"category_scores_gemma":[0.0002161259,0.0001139123,0.0000453229,0.0001982439,0.0001725117,0.0001052818,0.00003532309,0.0002416472,0.00001188735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001204764,"about_ca_system_score_gemma":0.00007790756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001068011,"about_ca_topic_score_gemma":0.000001598043,"domain_scores_codex":[0.9988068,0.0001043055,0.0003505875,0.0002815336,0.0002808805,0.0001759173],"domain_scores_gemma":[0.9992285,0.000161202,0.0002412366,0.0001849531,0.00007400149,0.0001101039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008417693,0.0009757584,0.05905256,0.0009305452,0.00003483894,0.00008410514,0.0003939918,0.00001639657,0.8605112,0.001878869,0.004358511,0.07167906],"study_design_scores_gemma":[0.002976829,0.0004074694,0.0437438,0.001188768,0.00009448238,0.00006236129,0.0001255434,0.824233,0.1252687,0.001105665,0.000550186,0.0002432261],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9590877,0.00005696643,0.03452697,0.005200902,0.0000515218,0.0007641222,0.000004242751,0.0001395822,0.0001679689],"genre_scores_gemma":[0.9911278,0.000005721072,0.008133215,0.0004761379,0.00006185191,0.0000694624,0.0000928582,0.00002093233,0.00001195163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8242166,"threshold_uncertainty_score":0.4645208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01210028878451519,"score_gpt":0.2796634066565475,"score_spread":0.2675631178720324,"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."}}