{"doi":"10.1109/trpms.2022.3194408","title":"Federated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data","abstract":"low-dose PET, is an efficient way to reduce radiation dose. However, low-dose PET reconstruction suffers from a low signal-to-noise ratio (SNR), affecting diagnosis and other PET-related applications. Recently, deep learning-based PET denoising methods have demonstrated superior performance in generating high-quality reconstruction. However, these methods require a large amount of representative data for training, which can be difficult to collect and share due to medical data privacy regulations. Moreover, low-dose PET data at different institutions may use different low-dose protocols, leading to non-identical data distribution. While previous federated learning (FL) algorithms enable multi-institution collaborative training without the need of aggregating local data, it is challenging for previous methods to address the large domain shift caused by different low-dose PET settings, and the application of FL to PET is still under-explored. In this work, we propose a federated transfer learning (FTL) framework for low-dose PET denoising using heterogeneous low-dose data. Our experimental results on simulated multi-institutional data demonstrate that our method can efficiently utilize heterogeneous low-dose data without compromising data privacy for achieving superior low-dose PET denoising performance for different institutions with different low-dose settings, as compared to previous FL methods.","journal":"IEEE Transactions on Radiation and Plasma Medical Sciences","year":2022,"id":250033,"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":27,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9446,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":688985,"name":"Tianshun Miao","orcid":"0000-0001-8121-4235","position":1,"is_corresponding":false},{"id":645560,"name":"Niloufar Mirian","orcid":null,"position":2,"is_corresponding":false},{"id":640076,"name":"Xiongchao Chen","orcid":"0000-0003-4112-8492","position":3,"is_corresponding":false},{"id":458712,"name":"Huidong Xie","orcid":"0000-0002-1124-3548","position":4,"is_corresponding":false},{"id":892188,"name":"Zhicheng Feng","orcid":null,"position":5,"is_corresponding":false},{"id":891417,"name":"Xueqi Guo","orcid":"0000-0002-0416-2811","position":6,"is_corresponding":false},{"id":228062,"name":"Xiaoxiao Li","orcid":"0000-0002-8833-0244","position":7,"is_corresponding":false},{"id":614770,"name":"S. Kevin Zhou","orcid":"0000-0002-6881-4444","position":8,"is_corresponding":false},{"id":228067,"name":"James S. Duncan","orcid":"0000-0002-5167-9856","position":9,"is_corresponding":false},{"id":267409,"name":"Chi Liu","orcid":"0000-0002-7007-1037","position":10,"is_corresponding":false},{"id":383206,"name":"Bo Zhou","orcid":"0000-0002-2906-0897","position":0,"is_corresponding":true}],"reference_count":81,"raw_metadata":null,"created_at":"2026-07-19T00:24:28.242323Z","pmid":"37789946","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":[]}