{"doi":"10.1002/mrm.28912","title":"Temporally aware volumetric generative adversarial network‐based MR image reconstruction with simultaneous respiratory motion compensation: Initial feasibility in 3D dynamic cine cardiac MRI","abstract":"PURPOSE: Develop a novel three-dimensional (3D) generative adversarial network (GAN)-based technique for simultaneous image reconstruction and respiratory motion compensation of 4D MRI. Our goal was to enable high-acceleration factors 10.7X-15.8X, while maintaining robust and diagnostic image quality superior to state-of-the-art self-gating (SG) compressed sensing wavelet (CS-WV) reconstruction at lower acceleration factors 3.5X-7.9X. METHODS: Our GAN was trained based on pixel-wise content loss functions, adversarial loss function, and a novel data-driven temporal aware loss function to maintain anatomical accuracy and temporal coherence. Besides image reconstruction, our network also performs respiratory motion compensation for free-breathing scans. A novel progressive growing-based strategy was adapted to make the training process possible for the proposed GAN-based structure. The proposed method was developed and thoroughly evaluated qualitatively and quantitatively based on 3D cardiac cine data from 42 patients. RESULTS: Our proposed method achieved significantly better scores in general image quality and image artifacts at 10.7X-15.8X acceleration than the SG CS-WV approach at 3.5X-7.9X acceleration (4.53 ± 0.540 vs. 3.13 ± 0.681 for general image quality, 4.12 ± 0.429 vs. 2.97 ± 0.434 for image artifacts, P < .05 for both). No spurious anatomical structures were observed in our images. The proposed method enabled similar cardiac-function quantification as conventional SG CS-WV. The proposed method achieved faster central processing unit-based image reconstruction (6 s/cardiac phase) than the SG CS-WV (312 s/cardiac phase). CONCLUSION: ) free-breathing 4D MR data acquisition with simultaneous respiratory motion compensation and fast reconstruction time.","journal":"Magnetic Resonance in Medicine","year":2021,"id":188500,"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":0.9529,"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":479489,"name":"Mark Bydder","orcid":"0000-0001-9210-0225","position":1,"is_corresponding":false},{"id":690976,"name":"Arash Bedayat","orcid":"0000-0001-8769-0224","position":2,"is_corresponding":false},{"id":479492,"name":"Ashley E. Prosper","orcid":"0000-0001-8615-5246","position":3,"is_corresponding":false},{"id":690975,"name":"Takegawa Yoshida","orcid":"0000-0002-2767-7009","position":4,"is_corresponding":false},{"id":396971,"name":"Kim‐Lien Nguyen","orcid":"0000-0002-8854-2976","position":5,"is_corresponding":false},{"id":690978,"name":"J. Paul Finn","orcid":"0000-0002-4534-7572","position":6,"is_corresponding":false},{"id":443145,"name":"Peng Hu","orcid":"0000-0002-0719-1159","position":7,"is_corresponding":false},{"id":479488,"name":"Vahid Ghodrati","orcid":"0000-0002-5964-333X","position":0,"is_corresponding":true}],"reference_count":75,"raw_metadata":null,"created_at":"2026-07-18T23:49:10.350554Z","pmid":"34254363","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":[]}