{"id":"W2914315180","doi":"10.1002/mp.13419","title":"Patient body motion correction for dynamic cardiac <scp>PET</scp>‐<scp>CT</scp> by attenuation‐emission alignment according to projection consistency conditions","year":2019,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; Carleton University; University of Ottawa","funders":"National Center for Advancing Translational Sciences; National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada","keywords":"Imaging phantom; Correction for attenuation; Cardiac PET; Nuclear medicine; Positron emission tomography; Projection (relational algebra); Iterative reconstruction; Partial volume; Computer science; Attenuation; Computer vision; Cardiac imaging; Data consistency; Torso; Artificial intelligence; Algorithm; Physics; Medicine; Radiology; Optics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004947751,0.0002771081,0.0004675976,0.0001033119,0.0002817216,0.00004686828,0.0001535406,0.0001259219,0.00002928915],"category_scores_gemma":[0.001809765,0.0002524556,0.0002287001,0.0004590987,0.0001056197,0.0001445691,0.00009715015,0.0004249614,0.0001496584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004047441,"about_ca_system_score_gemma":0.0002209195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005130398,"about_ca_topic_score_gemma":7.732785e-7,"domain_scores_codex":[0.9971397,0.00007359526,0.0005634133,0.0006084701,0.001163956,0.0004508678],"domain_scores_gemma":[0.9977393,0.0006775684,0.0002367062,0.0004647975,0.0003008491,0.0005807681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001285178,0.001428432,0.009103792,0.0003421357,0.0001642252,0.000007879654,0.0005000001,0.00002179578,0.1084624,0.0006861367,0.8150107,0.06425966],"study_design_scores_gemma":[0.005054976,0.003012849,0.006200909,0.002888765,0.001218017,0.0002158357,0.005179606,0.1524093,0.2541601,0.006756069,0.5623954,0.0005081624],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7188502,0.00006048206,0.2675131,0.003569097,0.001851388,0.004546768,0.0001685021,0.0005249387,0.00291554],"genre_scores_gemma":[0.9899108,0.00007840273,0.001651022,0.001897375,0.0003966589,0.001310678,0.001981371,0.00006370087,0.002709928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2710606,"threshold_uncertainty_score":0.9999928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01156988555147903,"score_gpt":0.2990573546424138,"score_spread":0.2874874690909348,"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."}}