{"doi":"10.1109/trpms.2025.3591035","title":"Randoms Estimation for Long Axial Field-of-View PET","abstract":"The high sensitivity of long axial field-of-view (AFOV) PET scanners has enabled studies over a wide range of count rates and count densities. However, these systems have a large axial acceptance angle that necessitates a wide coincidence window to capture the oblique true coincidences. In addition, the measured delays sinogram is sparse and noisy. We studied four methods of randoms estimation on a long AFOV system to assess their impact on accuracy and image noise: measured delays using a delayed coincidence window (RD), 2D Casey averaging of measured delays (RD-smooth), 2D average of measured delays (RD-ave - the current default method on the PennPET Explorer), and estimation of randoms from singles (RS). We looked at cases with varying count densities, randoms fractions, and non-pure positron emitters. A positive bias observed at low randoms counts for the RD and RD-smooth methods was not seen with the RD-ave or RS methods. For all cases, quantitative results with RS agreed to within 2.5% of the RD-ave method, while RD and RD-smooth estimates showed differences of 5-49%, with larger differences in areas of low uptake. The RS method is a practical technique for list-mode data and list-mode reconstruction by reducing the size of stored list events. It also avoids small approximations in the RD-ave method. For long AFOV systems, estimating randoms from singles is a practical and accurate method.","journal":"IEEE Transactions on Radiation and Plasma Medical Sciences","year":2025,"id":570644,"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":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9504,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"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":655575,"name":"Sarah E. Moore","orcid":"0000-0003-2481-6832","position":1,"is_corresponding":false},{"id":263429,"name":"Joel S. Karp","orcid":"0000-0002-6154-8585","position":2,"is_corresponding":false},{"id":367020,"name":"Margaret E. Daube-Witherspoon","orcid":"0000-0002-6318-2054","position":0,"is_corresponding":true}],"reference_count":0,"raw_metadata":null,"created_at":"2026-07-19T02:57:11.713851Z","pmid":"41267818","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":[]}