{"doi":"10.1109/trpms.2022.3187595","title":"Deep-Learning-Based Few-Angle Cardiac SPECT Reconstruction Using Transformer","abstract":"Convolutional neural networks (CNNs) have been extremely successful in various medical imaging tasks. However, because the size of the convolutional kernel used in a CNN is much smaller than the image size, CNN has a strong spatial inductive bias and lacks a global understanding of the input images. Vision Transformer, a recently emerged network structure in computer vision, can potentially overcome the limitations of CNNs for image-reconstruction tasks. In this work, we proposed a slice-by-slice Transformer network (SSTrans-3D) to reconstruct cardiac SPECT images from 3D few-angle data. To be specific, the network reconstructs the whole 3D volume using a slice-by-slice scheme. By doing so, SSTrans-3D alleviates the memory burden required by 3D reconstructions using Transformer. The network can still obtain a global understanding of the image volume with the Transformer attention blocks. Lastly, already reconstructed slices are used as the input to the network so that SSTrans-3D can potentially obtain more informative features from these slices. Validated on porcine, phantom, and human studies acquired using a GE dedicated cardiac SPECT scanner, the proposed method produced images with clearer heart cavity, higher cardiac defect contrast, and more accurate quantitative measurements on the testing data as compared with a deep U-net.","journal":"IEEE Transactions on Radiation and Plasma Medical Sciences","year":2022,"id":256564,"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":19,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9494,"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":480904,"name":"Stephanie Thorn","orcid":"0000-0003-0020-0814","position":1,"is_corresponding":false},{"id":267408,"name":"Yi-Hwa Liu","orcid":"0000-0003-4211-8763","position":2,"is_corresponding":false},{"id":890401,"name":"Supum Lee","orcid":"0000-0002-5017-9032","position":3,"is_corresponding":false},{"id":480905,"name":"Zhao Liu","orcid":"0000-0002-9847-7098","position":4,"is_corresponding":false},{"id":277821,"name":"Ge Wang","orcid":"0000-0002-2656-7705","position":5,"is_corresponding":false},{"id":316504,"name":"Albert J. Sinusas","orcid":"0000-0003-0972-9589","position":6,"is_corresponding":false},{"id":267409,"name":"Chi Liu","orcid":"0000-0002-7007-1037","position":7,"is_corresponding":false},{"id":458712,"name":"Huidong Xie","orcid":"0000-0002-1124-3548","position":0,"is_corresponding":true}],"reference_count":30,"raw_metadata":null,"created_at":"2026-07-19T00:25:25.728895Z","pmid":"37397179","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":[]}