{"doi":"10.1007/s00259-025-07182-6","title":"Impact of deep learning denoising on kinetic modelling for low-dose dynamic PET: application to single- and dual-tracer imaging protocols","abstract":"Abstract Purpose Long-axial field-of-view PET scanners capture multi-organ tracer distribution with high sensitivity, enabling lower dose dynamic protocols and dual-tracer imaging for comprehensive disease characterization. However, reducing dose may compromise data quality and time-activity curve (TAC) fitting, leading to higher bias in kinetic parameters. Parametric imaging poses further challenges due to noise amplification in voxel-based modelling. We explore the potential of deep learning denoising (DL-DN) to improve quantification for low-dose dynamic PET. Methods Using 16 [ 18 F]FDG PET studies from the PennPET Explorer, we trained a DL framework on 10-min images from late-phase uptake (static data) that were sub-sampled from 1/2 to 1/300 of the counts. This model was used to denoise early-to-late dynamic frame images. Its impact on quantification was evaluated using compartmental modelling and voxel-based graphical analysis for parametric imaging for single- and dual-tracer dynamic studies with [ 18 F]FDG and [ 18 F]FGln at original (injected) and reduced (sub-sampled) doses. Quantification differences were evaluated for the area under the curve of TACs, K i for [ 18 F]FDG and V T for [ 18 F]FGln, and parametric images. Results DL-DN consistently improved image quality across all dynamic frames, systematically enhancing TAC consistency and reducing tissue-dependent bias and variability in K i and V T down to 40 MBq doses. DL-DN preserved tumor heterogeneity in Logan V T images and delineation of high-flux regions in Patlak K i maps. In a /[ 18 F]FDG dual-tracer study, bias trends aligned with single-tracer results but showed reduced accuracy for [¹⁸F]FGln in breast lesions at very low doses (4 MBq). Conclusion This study demonstrates that applying DL-DN trained on static [ 18 F]FDG PET images to dynamic [ 18 F]FDG and [ 18 F]FGln PET can permit significantly reduced doses, preserving accurate FDG K i and FGln V T measurements, and enhancing parametric image quality. DL-DN shows promise for improving dynamic PET quantification at reduced doses, including novel dual-tracer studies.","journal":"European Journal of Nuclear Medicine and Molecular Imaging","year":2025,"id":512038,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9487,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":656400,"name":"Elizabeth Li","orcid":"0000-0003-2365-0594","position":1,"is_corresponding":false},{"id":367020,"name":"Margaret E. Daube-Witherspoon","orcid":"0000-0002-6318-2054","position":2,"is_corresponding":false},{"id":263428,"name":"Austin R. Pantel","orcid":"0000-0001-8649-9970","position":3,"is_corresponding":false},{"id":301109,"name":"Corinde E. Wiers","orcid":"0000-0002-2934-8794","position":4,"is_corresponding":false},{"id":332202,"name":"Jacob G. Dubroff","orcid":"0000-0002-0732-2374","position":5,"is_corresponding":false},{"id":402137,"name":"Christian Vanhove","orcid":"0000-0002-3988-5980","position":6,"is_corresponding":false},{"id":237780,"name":"Stefaan Vandenberghe","orcid":"0000-0002-2377-3968","position":7,"is_corresponding":false},{"id":263429,"name":"Joel S. Karp","orcid":"0000-0002-6154-8585","position":8,"is_corresponding":false},{"id":1370902,"name":"Florence M. Muller","orcid":"0000-0001-5287-7355","position":0,"is_corresponding":true}],"reference_count":52,"raw_metadata":null,"created_at":"2026-07-19T02:48:01.269605Z","pmid":"40069458","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}