{"id":"W4205766680","doi":"10.1007/s11517-021-02477-w","title":"Abdominal motion tracking with free-breathing XD-GRASP acquisitions using spatio-temporal geodesic trajectories","year":2022,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Philips (Canada); Polytechnique Montréal; CARE Canada; Centre Hospitalier de l’Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"GRASP; Human physiology; Computer vision; Motion (physics); Geodesic; Tracking (education); Artificial intelligence; Computer science; Breathing; Anatomy; Mathematics; Mathematical analysis; Medicine; Psychology","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":[0.0002891708,0.0004160988,0.0002529226,0.0004456237,0.0002016043,0.000583005,0.0002853996,0.0005629741,0.001303802],"category_scores_gemma":[0.001252954,0.0002829716,0.0002690517,0.0005677803,0.0002185978,0.0004532886,0.0006433838,0.0003760398,0.0003775373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001492913,"about_ca_system_score_gemma":0.0004760498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001573542,"about_ca_topic_score_gemma":0.001857852,"domain_scores_codex":[0.9998975,0.0000218433,0.000007944474,0.00002173425,0.00003709166,0.00001388157],"domain_scores_gemma":[0.9998212,0.00004678819,0.00003444911,0.00003307862,0.00004661445,0.00001786767],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00191029,0.000224324,0.01287594,0.0007388495,0.0002101687,0.001370218,0.0008597636,0.1874551,0.3671163,0.008855138,0.004595845,0.4137881],"study_design_scores_gemma":[0.00008193475,0.0004939241,0.02204114,0.0001021926,0.00009146681,0.002336372,0.0002834908,0.85805,0.1062157,0.003813266,0.006405442,0.0000850209],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.246517,0.0007051171,0.745956,0.0003125058,0.00008881978,0.0001196612,0.000576783,0.00136669,0.004357527],"genre_scores_gemma":[0.7805628,0.0004908688,0.2151506,0.0000750848,0.00002758883,0.0000742162,0.0005976087,0.0001874431,0.002833828],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001573542,"threshold_uncertainty_score":0.004361629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02801098325872214,"score_gpt":0.2854288979675326,"score_spread":0.2574179147088104,"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."}}