{"doi":"10.3389/fphy.2021.684184","title":"Comparison of Complex k-Space Data and Magnitude-Only for Training of Deep Learning–Based Artifact Suppression for Real-Time Cine MRI","abstract":"Propose: The purpose of this study was to compare the performance of deep learning networks trained with complex-valued and magnitude images in suppressing the aliasing artifact for highly accelerated real-time cine MRI. Methods: Two 3D U-net models (Complex-Valued-Net and Magnitude-Net) were implemented to suppress aliasing artifacts in real-time cine images. ECG-segmented cine images ( n = 503) generated from both complex k-space data and magnitude-only DICOM were used to synthetize radial real-time cine MRI. Complex-Valued-Net and Magnitude-Net were trained with fully sampled and synthetized radial real-time cine pairs generated from highly undersampled (12-fold) complex k -space and DICOM images, respectively. Real-time cine was prospectively acquired in 29 patients with 12-fold accelerated free-breathing tiny golden-angle radial sequence and reconstructed with both Complex-Valued-Net and Magnitude-Net. Cardiac function, left-ventricular (LV) structure, and subjective image quality [1(non-diagnostic)-5(excellent)] were calculated from Complex-Valued-Net– and Magnitude-Net–reconstructed real-time cine datasets and compared to those of ECG-segmented cine (reference). Results: Free-breathing real-time cine reconstructed by both networks had high correlation (all R 2 > 0.7) and good agreement (all p > 0.05) with standard clinical ECG-segmented cine with respect to LV function and structural parameters. Real-time cine reconstructed by Complex-Valued-Net had superior image quality compared to images from Magnitude-Net in terms of myocardial edge sharpness (Complex-Valued-Net = 3.5 ± 0.5; Magnitude-Net = 2.6 ± 0.5), temporal fidelity (Complex-Valued-Net = 3.1 ± 0.4; Magnitude-Net = 2.1 ± 0.4), and artifact suppression (Complex-Valued-Net = 3.1 ± 0.5; Magnitude-Net = 2.0 ± 0.0), which were all inferior to those of ECG-segmented cine (4.1 ± 1.4, 3.9 ± 1.0, and 4.0 ± 1.1). Conclusion: Compared to Magnitude-Net, Complex-Valued-Net produced improved subjective image quality for reconstructed real-time cine images and did not show any difference in quantitative measures of LV function and structure.","journal":"Frontiers in Physics","year":2021,"id":195713,"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":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8794,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":489830,"name":"Rui Guo","orcid":"0000-0002-5188-6281","position":1,"is_corresponding":false},{"id":317790,"name":"Selçuk Küçükseymen","orcid":"0000-0002-9757-3088","position":2,"is_corresponding":false},{"id":767671,"name":"Yankama Tuyen","orcid":null,"position":3,"is_corresponding":false},{"id":351704,"name":"Jennifer Rodriguez","orcid":"0000-0002-6291-9173","position":4,"is_corresponding":false},{"id":352465,"name":"Amanda Paskavitz","orcid":null,"position":5,"is_corresponding":false},{"id":354010,"name":"Patrick Pierce","orcid":"0000-0002-0774-1827","position":6,"is_corresponding":false},{"id":354011,"name":"Beth Goddu","orcid":"0009-0006-2347-2686","position":7,"is_corresponding":false},{"id":310909,"name":"Long Ngo","orcid":"0000-0001-9349-7981","position":8,"is_corresponding":false},{"id":317794,"name":"Reza Nezafat","orcid":"0000-0002-1963-7138","position":9,"is_corresponding":false},{"id":317793,"name":"Hassan Haji‐Valizadeh","orcid":"0000-0002-7652-1748","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-18T23:50:07.691929Z","pmid":null,"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":[]}