{"doi":"10.1002/alz.12564","title":"Deep residual inception encoder‐decoder network for amyloid PET harmonization","abstract":"INTRODUCTION: Multiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis. We accordingly developed and validated a deep learning model as a harmonization strategy. METHOD: A Residual Inception Encoder-Decoder Neural Network was developed to harmonize images between amyloid PET image pairs made with Pittsburgh Compound-B and florbetapir tracers. The model was trained using a dataset with 92 subjects with 10-fold cross validation and its generalizability was further examined using an independent external dataset of 46 subjects. RESULTS: Significantly stronger between-tracer correlations (P < .001) were observed after harmonization for both global amyloid burden indices and voxel-wise measurements in the training cohort and the external testing cohort. DISCUSSION: We proposed and validated a novel encoder-decoder based deep model to harmonize amyloid PET imaging data from different tracers. Further investigation is ongoing to improve the model and apply to additional tracers.","journal":"Alzheimer s & Dementia","year":2022,"id":269152,"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":16,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9461,"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":320016,"name":"Fei Gao","orcid":"0000-0001-5675-1899","position":1,"is_corresponding":false},{"id":931904,"name":"Baoxin Li","orcid":"0000-0002-9294-4572","position":2,"is_corresponding":false},{"id":255195,"name":"Valentina Ghisays","orcid":"0000-0002-8434-9407","position":3,"is_corresponding":false},{"id":320019,"name":"Ji Luo","orcid":"0000-0001-5504-3129","position":4,"is_corresponding":false},{"id":255185,"name":"Yinghua Chen","orcid":"0000-0002-5020-7507","position":5,"is_corresponding":false},{"id":407158,"name":"Wendy Lee","orcid":"0000-0001-9723-9779","position":6,"is_corresponding":false},{"id":477666,"name":"Yuxiang Zhou","orcid":"0000-0002-3462-2305","position":7,"is_corresponding":false},{"id":301875,"name":"Tammie L.S. Benzinger","orcid":"0000-0002-8114-0552","position":8,"is_corresponding":false},{"id":55006,"name":"Eric M. Reiman","orcid":"0000-0002-0705-3696","position":9,"is_corresponding":false},{"id":226061,"name":"Kewei Chen","orcid":"0000-0001-8497-3069","position":10,"is_corresponding":false},{"id":255186,"name":"Yi Su","orcid":"0000-0002-1946-8063","position":11,"is_corresponding":false},{"id":320020,"name":"Teresa Wu","orcid":"0000-0002-0529-7048","position":12,"is_corresponding":false},{"id":877278,"name":"Jay Shah","orcid":"0000-0002-3617-2395","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":null,"created_at":"2026-07-19T00:27:18.142851Z","pmid":"35142053","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":[]}