{"id":"W4408290387","doi":"10.1007/s10278-025-01469-8","title":"SADiff: A Sinogram-Aware Diffusion Model for Low-Dose CT Image Denoising","year":2025,"lang":"en","type":"article","venue":"Journal of Imaging Informatics in Medicine","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; Saskatchewan Health Authority; University of Waterloo; University of Toronto","funders":"","keywords":"Image denoising; Diffusion; Noise reduction; Artificial intelligence; Nuclear medicine; Computer science; Computer vision; Medicine; Medical physics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001374931,0.0001945017,0.0006638375,0.0007543485,0.00009742902,0.00003346798,0.0002694885,0.00004404907,0.00001878344],"category_scores_gemma":[0.001118605,0.0001388504,0.0001444581,0.0004621715,0.0002743293,0.0002907532,0.00007684928,0.0006339303,0.000001546639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002052098,"about_ca_system_score_gemma":0.0002903195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001261679,"about_ca_topic_score_gemma":0.000001286729,"domain_scores_codex":[0.997497,0.00001762752,0.001601693,0.00009481112,0.0004737869,0.000315053],"domain_scores_gemma":[0.9981676,0.0002430318,0.0005675732,0.0003275449,0.0004911567,0.0002030584],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001124663,0.002137314,0.02417031,0.009980791,0.0003430518,0.0004477941,0.01337551,0.001142131,0.05952461,0.005017838,0.5703368,0.3123992],"study_design_scores_gemma":[0.005231678,0.0001279752,0.0006805126,0.006820903,0.0002342933,0.0004773866,0.001490566,0.9725129,0.001115272,0.004799504,0.006366737,0.0001422424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09214868,0.0002912806,0.8794858,0.02560765,0.00020113,0.00062824,0.000004893801,0.00005452281,0.001577729],"genre_scores_gemma":[0.71736,0.0004458895,0.2733028,0.008213346,0.0002382372,0.00003034945,0.00002675832,0.00002692644,0.0003557296],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9713708,"threshold_uncertainty_score":0.5662155,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02040922687265996,"score_gpt":0.3653445969416198,"score_spread":0.3449353700689599,"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."}}