{"id":"W4417471557","doi":"10.1109/nss/mic/rtsd57106.2025.11286874","title":"Maximum Likelihood Reconstruction of Attenuation and Activity (MLAA) in SPECT for Improved Attenuation Correction: Simulation, Phantom, and Patient Data Validation","year":2025,"lang":"","type":"article","venue":"","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Attenuation; Correction for attenuation; Energy (signal processing); Patient data; Bilinear interpolation; Maximum likelihood; Data validation","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":[0.005212334,0.001050395,0.0007942492,0.0007583961,0.0004101281,0.001204917,0.00104308,0.001232292,0.001157045],"category_scores_gemma":[0.01339826,0.0006442475,0.0007886697,0.0008480452,0.0006407718,0.0005650478,0.001115088,0.001050164,0.0005068383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007423505,"about_ca_system_score_gemma":0.001770625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004935598,"about_ca_topic_score_gemma":0.003364607,"domain_scores_codex":[0.9982881,0.001180793,0.00008680905,0.0001016602,0.0002873042,0.00005536685],"domain_scores_gemma":[0.9942051,0.004544877,0.0003189892,0.000355561,0.000473417,0.0001021819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008054506,0.0002903006,0.004711004,0.0003403285,0.0001289839,0.0002397733,0.0002744849,0.9248795,0.009927661,0.00266122,0.001288723,0.05445261],"study_design_scores_gemma":[0.00005417505,0.000103483,0.0005759801,0.00001543923,0.00001284797,0.0001149495,0.00001300547,0.9893105,0.008576964,0.0006099683,0.0005950783,0.00001772221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1457375,0.0007523615,0.8482048,0.0003726501,0.0000351141,0.000292126,0.0006414187,0.002470314,0.001493735],"genre_scores_gemma":[0.5392392,0.0003049847,0.4573247,0.0001471365,0.00001597947,0.0004784046,0.00106198,0.0005722049,0.0008552629],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005212334,"threshold_uncertainty_score":0.02756578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03499785763555715,"score_gpt":0.3432443896130818,"score_spread":0.3082465319775247,"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."}}