{"id":"W2046477252","doi":"10.1118/1.3611911","title":"SU‐E‐J‐143: Respiratory‐Correlated Cone‐Beam CT for Potential Use in Measuring Lung Tumor Trajectory","year":2011,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"","keywords":"Imaging phantom; Cone beam computed tomography; Image registration; Nuclear medicine; Centroid; Truebeam; Trajectory; Projection (relational algebra); Breathing; Image-guided radiation therapy; Lung cancer; Streak; Medical imaging; Rotation (mathematics); Computer science; Iterative reconstruction; Linear particle accelerator; Computer vision; Physics; Medicine; Beam (structure); Artificial intelligence; Optics; Computed tomography; Radiology; Image (mathematics); Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001457824,0.0003798512,0.0003097827,0.0007166271,0.0001443004,0.0007160836,0.0007667639,0.0007547939,0.003565361],"category_scores_gemma":[0.002359254,0.0005880928,0.0002719018,0.0006973536,0.0002317932,0.0005671743,0.0002661833,0.0006243293,0.001053061],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002599286,"about_ca_system_score_gemma":0.0006479402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009472427,"about_ca_topic_score_gemma":0.001712646,"domain_scores_codex":[0.9996176,0.00009476172,0.00002384024,0.00005716511,0.0001840692,0.00002252293],"domain_scores_gemma":[0.999278,0.0002158436,0.0001034177,0.000139988,0.0001977423,0.00006497428],"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":[0.002206742,0.0001881241,0.01716444,0.000548146,0.0001121237,0.0006550677,0.00007671203,0.003962967,0.7579967,0.00191736,0.0044075,0.2107641],"study_design_scores_gemma":[0.0004043785,0.001958474,0.1265348,0.0002312989,0.0002808846,0.009961106,0.00007380711,0.1193502,0.7127917,0.0009792313,0.02723225,0.0002018447],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4310634,0.00668378,0.5277206,0.00066723,0.0002209065,0.0005546618,0.001887146,0.00959743,0.02160484],"genre_scores_gemma":[0.5986729,0.001144582,0.3904141,0.0004218742,0.00004094173,0.0003127704,0.002130714,0.001263182,0.005599005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003565361,"threshold_uncertainty_score":0.01192737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03291451097001837,"score_gpt":0.2712306497812668,"score_spread":0.2383161388112485,"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."}}