{"id":"W59016446","doi":"10.1007/978-3-319-14678-2_2","title":"Deformable Image Registration and Intensity Correction of Cardiac Perfusion MRI","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Ontario Institute of Technology","keywords":"Hessian matrix; Jacobian matrix and determinant; Regularization (linguistics); Image registration; Artificial intelligence; Intensity (physics); Computer science; Transformation (genetics); Magnetic resonance imaging; Dynamic contrast-enhanced MRI; Computer vision; Algorithm; Mathematics; Image (mathematics); Medicine; Radiology; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007065847,0.0007742019,0.0008686517,0.001104992,0.0002243545,0.001140861,0.001749888,0.001134284,0.006281518],"category_scores_gemma":[0.001954511,0.0008306678,0.001023568,0.001694475,0.000696381,0.001055358,0.001029286,0.001665033,0.004444711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004439403,"about_ca_system_score_gemma":0.0005154398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001173466,"about_ca_topic_score_gemma":0.001727542,"domain_scores_codex":[0.9994681,0.00007441772,0.00003228315,0.0001299994,0.0002617279,0.00003336585],"domain_scores_gemma":[0.9995525,0.0001787181,0.00004161782,0.0001256825,0.00008461259,0.00001686552],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006505745,0.00003626681,0.0001671026,0.0003568635,0.00006229133,0.0001624301,0.0001066997,0.02690452,0.06867874,0.02557497,0.009500762,0.8683843],"study_design_scores_gemma":[0.00002511998,0.000196245,0.00270134,0.0001485681,0.000121383,0.003632148,0.0000841188,0.6143205,0.173019,0.05609086,0.1495216,0.0001390875],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002287648,0.003049736,0.9881232,0.0001876484,0.0002237778,0.00004867979,0.0000924352,0.001190489,0.004796358],"genre_scores_gemma":[0.06139324,0.008380358,0.8853255,0.0001988679,0.0003873043,0.0001081707,0.0006932533,0.001427338,0.04208589],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006281518,"threshold_uncertainty_score":0.0210138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01063067186432116,"score_gpt":0.2641699702286711,"score_spread":0.2535392983643499,"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."}}