{"id":"W2039034838","doi":"10.1117/12.480377","title":"Interactive volume rendering of multimodality 4D cardiac data with the use of consumer graphics hardware","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Computer Graphics and Visualization Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Volume rendering; Rendering (computer graphics); Multimodality; Computer graphics (images); Visualization; Interactive visualization; Modalities; Graphics; Software rendering; Software; Computer graphics; Graphics hardware; Volume (thermodynamics); Data visualization; Computer vision; Artificial intelligence; 3D computer graphics","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":[0.0008665869,0.0007713939,0.0005430865,0.0008782173,0.0002115638,0.001680004,0.00115597,0.0005873059,0.0129749],"category_scores_gemma":[0.003349195,0.0004444342,0.0006324379,0.0006574531,0.0004612527,0.0008951742,0.001552805,0.0009086906,0.001361641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000295886,"about_ca_system_score_gemma":0.0003818037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006472468,"about_ca_topic_score_gemma":0.001076803,"domain_scores_codex":[0.9995452,0.0001191842,0.00003050685,0.00003854647,0.0002317553,0.00003488148],"domain_scores_gemma":[0.9983761,0.001051992,0.0000715765,0.0002840709,0.0001454758,0.00007076737],"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":[0.0008256526,0.0002174502,0.002057578,0.0008081174,0.0001593934,0.001716943,0.00124384,0.08886483,0.3586456,0.02124144,0.02435479,0.4998643],"study_design_scores_gemma":[0.0003016141,0.0004536521,0.003734502,0.000138278,0.00009569545,0.003861552,0.0003070905,0.6690841,0.216938,0.0319064,0.07291123,0.0002677533],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01648902,0.0001871693,0.9743557,0.0002260899,0.00007872021,0.0001149827,0.0003649492,0.004681751,0.003501642],"genre_scores_gemma":[0.1619223,0.0006153401,0.8306285,0.0001708785,0.0001105169,0.0002764563,0.0008165903,0.001862984,0.003596297],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0129749,"threshold_uncertainty_score":0.04340541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03419306831232906,"score_gpt":0.2680007623300698,"score_spread":0.2338076940177408,"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."}}