{"doi":"10.1088/1361-6560/ad1b6a","title":"Motion artifact correction in cardiac CT using cross-phase temporospatial information and synergistic attention gate and spatial transformer sub-networks","abstract":"Abstract Objectives. To improve quality of coronary CT angiography (CCTA) images using a generalizable motion-correction algorithm. Approach . A neural network with attention gate and spatial transformer (ATOM) was developed to correct coronary motion. Phantom and patient CCTA images (39 males, 32 females, age range 19–92, scan date 02/2020 to 10/2021) retrospectively collected from dual-source CT were used to create training, development, and testing sets corresponding to 140- and 75 ms temporal resolution, with 75 ms images as labels. To test generalizability, ATOM was deployed for locally adaptive motion-correction in both 140- and 75 ms patient images. Objective metrics were used to assess motion-corrupted and corrected phantom and patient images, including structural-similarity-index (SSIM), dice-similarity-coefficient (DSC), peak-signal-noise-ratio (PSNR), and normalized root-mean-square-error (NRMSE). In objective quality assessment, ATOM was compared with several baseline networks, including U-net, U-net plus attention gate, U-net plus spatial transformer, VDSR, and ResNet. Two cardiac radiologists independently interpreted motion-corrupted and -corrected images at 75 and 140 ms in a blinded fashion and ranked diagnostic image quality (worst to best: 1–4, no ties). Main results . ATOM improved quality metrics ( p < 0.05) before/after correction: in phantom, SSIM 0.87/0.95, DSC 0.85/0.93, PSNR 19.4/22.5, NRMSE 0.38/0.27; in patient images, SSIM 0.82/0.88, DSC 0.88/0.90, PSNR 30.0/32.0, NRMSE 0.16/0.12. ATOM provided more consistent improvement of objective image quality, compared to the presented baseline networks. The motion-corrected images received better ranks than un-corrected at the same temporal resolution ( p < 0.05): 140 ms images 1.65/2.25, and 75 ms images 3.1/3.2. The motion-corrected 75 ms images received the best rank in 65% of testing cases. A fair-to-good inter-reader agreement was observed (Kappa score 0.58). Significance . ATOM reduces motion artifacts, improving visualization of coronary arteries. This algorithm can be used to virtually improve temporal resolution in both single- and dual-source CT.","journal":"Physics in Medicine and Biology","year":2024,"id":455114,"datarank":0.29188652235829704,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.0,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":6,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9471,"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":625630,"name":"Zaki Ahmed","orcid":"0000-0001-5648-0590","position":1,"is_corresponding":false},{"id":577751,"name":"Shaojie Chang","orcid":"0000-0003-0550-9650","position":2,"is_corresponding":false},{"id":1033639,"name":"Emily K. Koons","orcid":"0000-0001-6646-595X","position":3,"is_corresponding":false},{"id":908277,"name":"Jamison E. Thorne","orcid":"0000-0003-3993-6423","position":4,"is_corresponding":false},{"id":264736,"name":"Prabhakar Rajiah","orcid":"0000-0001-7538-385X","position":5,"is_corresponding":false},{"id":1057257,"name":"Thomas A. Foley","orcid":"0000-0003-0347-7931","position":6,"is_corresponding":false},{"id":237064,"name":"Joel G. Fletcher","orcid":"0000-0002-8941-5434","position":7,"is_corresponding":false},{"id":237065,"name":"Cynthia H. McCollough","orcid":"0000-0002-5346-332X","position":8,"is_corresponding":false},{"id":237063,"name":"Shuai Leng","orcid":"0000-0002-6453-9481","position":9,"is_corresponding":false},{"id":355872,"name":"Hao Gong","orcid":"0000-0002-1123-7172","position":0,"is_corresponding":true}],"reference_count":30,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:03:17.458329Z","pmid":"38181426","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":[]}