{"id":"W2417699103","doi":"10.1007/s10439-016-1654-y","title":"Restoration of Thickness, Density, and Volume for Highly Blurred Thin Cortical Bones in Clinical CT Images","year":2016,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sunnybrook Health Science Centre; Sunnybrook Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Council on Graduate Studies, Council of Ontario Universities","keywords":"Deblurring; Imaging phantom; Deconvolution; Point spread function; Cortical bone; Conjugate gradient method; Cadaveric spasm; Biomedical engineering; Scanner; Materials science; Iterative reconstruction; Tomography; Image restoration; Mathematics; Nuclear medicine; Computer science; Artificial intelligence; Image processing; Algorithm; Optics; Physics; Anatomy; Image (mathematics); Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001770115,0.0008372974,0.0004373152,0.002668122,0.0003276134,0.001686965,0.000586114,0.0009493536,0.001137694],"category_scores_gemma":[0.007220851,0.0007731966,0.0004770313,0.0008631961,0.000661323,0.001000578,0.0007726343,0.0009397424,0.0004594732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003596341,"about_ca_system_score_gemma":0.001097637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004831635,"about_ca_topic_score_gemma":0.00388058,"domain_scores_codex":[0.9996696,0.00007988626,0.00003936524,0.0000346007,0.0001380392,0.00003864595],"domain_scores_gemma":[0.9983595,0.000591534,0.0003298272,0.0002354122,0.0003664441,0.0001173433],"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":[0.003619601,0.0002559418,0.03035139,0.001789477,0.0002981255,0.002255258,0.001791231,0.09980705,0.3006452,0.005500966,0.004115128,0.5495707],"study_design_scores_gemma":[0.0001194703,0.000359461,0.06863588,0.0003693344,0.0005775048,0.01139789,0.0008929506,0.6905234,0.2106095,0.008806686,0.00742965,0.0002782801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4981705,0.005005078,0.4898448,0.0009164766,0.0001417455,0.000190039,0.0006731489,0.003261431,0.001796711],"genre_scores_gemma":[0.8334795,0.002980383,0.1611091,0.0001308544,0.000104686,0.00005492191,0.0004128291,0.0005196613,0.001207961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004831635,"threshold_uncertainty_score":0.009607017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0499668252889527,"score_gpt":0.3671578562063142,"score_spread":0.3171910309173615,"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."}}