{"doi":"10.1109/tmi.2024.3514925","title":"POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation","abstract":"Low-dose PET offers a valuable means of minimizing radiation exposure in PET imaging. However, the prevalent practice of employing additional CT scans for generating attenuation maps ( -map) for PET attenuation correction significantly elevates radiation doses. To address this concern and further mitigate radiation exposure in low-dose PET exams, we propose an innovative Population-prior-aided Over-Under-Representation Network (POUR-Net) that aims for high-quality attenuation map generation from low-dose PET. First, POUR-Net incorporates an Over-Under-Representation Network (OUR-Net) to facilitate efficient feature extraction, encompassing both low-resolution abstracted and fine-detail features, for assisting deep generation on the full-resolution level. Second, complementing OUR-Net, a population prior generation machine (PPGM) utilizing a comprehensive CT-derived -map dataset, provides additional prior information to aid OUR-Net generation. The integration of OUR-Net and PPGM within a cascade framework enables iterative refinement of -map generation, resulting in the production of high-quality -maps. Experimental results underscore the effectiveness of POUR-Net, showing it as a promising solution for accurate CT-free low-count PET attenuation correction, which also surpasses the performance of previous baseline methods.","journal":"IEEE Transactions on Medical Imaging","year":2024,"id":451126,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9439,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":502865,"name":"Jun Hou","orcid":"0000-0003-4089-6086","position":1,"is_corresponding":false},{"id":270585,"name":"Tianqi Chen","orcid":"0000-0002-8762-9682","position":2,"is_corresponding":false},{"id":1020624,"name":"Yinchi Zhou","orcid":null,"position":3,"is_corresponding":false},{"id":640076,"name":"Xiongchao Chen","orcid":"0000-0003-4112-8492","position":4,"is_corresponding":false},{"id":458712,"name":"Huidong Xie","orcid":"0000-0002-1124-3548","position":5,"is_corresponding":false},{"id":890854,"name":"Qiong Liu","orcid":"0000-0001-8153-233X","position":6,"is_corresponding":false},{"id":891417,"name":"Xueqi Guo","orcid":"0000-0002-0416-2811","position":7,"is_corresponding":false},{"id":1241234,"name":"Menghua Xia","orcid":"0000-0002-9503-1381","position":8,"is_corresponding":false},{"id":676632,"name":"Yu‐Jung Tsai","orcid":"0000-0003-1749-7917","position":9,"is_corresponding":false},{"id":1179658,"name":"Vladimir Panin","orcid":null,"position":10,"is_corresponding":false},{"id":235256,"name":"Takuya Toyonaga","orcid":"0000-0002-0369-8294","position":11,"is_corresponding":false},{"id":228067,"name":"James S. Duncan","orcid":"0000-0002-5167-9856","position":12,"is_corresponding":false},{"id":267409,"name":"Chi Liu","orcid":"0000-0002-7007-1037","position":13,"is_corresponding":false},{"id":383206,"name":"Bo Zhou","orcid":"0000-0002-2906-0897","position":0,"is_corresponding":true}],"reference_count":61,"raw_metadata":null,"created_at":"2026-07-19T02:02:41.417944Z","pmid":"40030468","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":[]}