{"id":"W2120963878","doi":"10.1109/10.821781","title":"Three-dimensional blind deconvolution of SPECT images","year":2000,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Deconvolution; Artificial intelligence; Computer science; Single-photon emission computed tomography; Computer vision; Blind deconvolution; Image resolution; Inverse problem; Spect imaging; Iterative reconstruction; Constraint (computer-aided design); Image segmentation; Medical imaging; Pattern recognition (psychology); Segmentation; Algorithm; Mathematics; Nuclear medicine","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001206745,0.0001287667,0.0002180732,0.0001793047,0.00004558947,0.000004772518,0.00007763472,0.00009260947,0.002088019],"category_scores_gemma":[0.00001020031,0.0001123842,0.0001225232,0.0003485176,0.0001407097,0.00004071753,7.666339e-7,0.0003140153,0.00007079599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004676866,"about_ca_system_score_gemma":0.00005062118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003938139,"about_ca_topic_score_gemma":0.000001136071,"domain_scores_codex":[0.9989527,0.000004918704,0.0002966508,0.0002049375,0.0003419236,0.0001988567],"domain_scores_gemma":[0.9993891,0.0000693287,0.00002476604,0.0002380013,0.00003356483,0.0002452184],"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":[0.000555331,0.003097271,0.00003306413,0.0003713744,0.000349339,0.0001001585,0.00006235893,0.009177919,0.4738986,0.0003467888,0.01449507,0.4975128],"study_design_scores_gemma":[0.004529269,0.00100692,0.001490156,0.0009416858,0.0003393769,0.0005273174,0.000008074393,0.5535626,0.3949612,0.0002415809,0.04182203,0.0005697607],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1245725,0.00004923937,0.8720905,0.002379915,0.0001315338,0.0002554153,0.00003173994,0.0002855935,0.000203495],"genre_scores_gemma":[0.963913,0.00006863762,0.03546701,0.0001394195,0.00009255857,0.00004641314,0.00001338284,0.00002274001,0.000236885],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8393404,"threshold_uncertainty_score":0.9988242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01413741239405569,"score_gpt":0.2658410584288332,"score_spread":0.2517036460347775,"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."}}