{"doi":"10.1088/1361-6560/abc04f","title":"Deep convolution neural networks based artifact suppression in under-sampled radial acquisitions of myocardial <i>T</i> <sub>1</sub> mapping images","abstract":"Abstract We developed a deep convolutional neural network (CNN) based method to remove streaking artefact from accelerated radial acquisitions of myocardial T 1 -mapping images. A deep CNN based on a modified U-Net architecture was developed and trained to remove the streaking artefacts from under-sampled T 1 mapping images. A total of 2090 T 1 -weighted images for 33 patients (55 ± 15 years, 19 males) and five healthy subjects (30 ± 14 years, 2 males) were used for training and testing the network. The images were acquired using radial slice interleaved T 1 mapping sequence (STONE) and retrospectively under-sampled to achieve acceleration rate of 4 (corresponding to 48 spokes). The dataset was split into training and testing subsets with 23 subjects (60%) and 15 subjects (40%), respectively. For generating voxel-wise T 1 maps, a two-parameter fitting model was used. Network performance was evaluated using normalized mean square error (NMSE), structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) metrics. The proposed network allowed fast (&lt;0.3 s/image) removal of the artefact from all T 1 -weighted testing images and the corresponding T 1 maps with PSNR = 64.3 ± 1.02, NMSE = 0.2 ± 0.09 and SSIM = 0.9 ± 0.3 × 10 −4 . There was no statistically significant difference between the measured T 1 maps for both per-subject (reference: 1085 ± 37 ms, CNN: 1088 ± 37 ms, p = 0.4) and per-segment (reference: 1084 ± 48 ms, CNN: 1083 ± 58 ms, p = 0.9) analyses. In summary, deep CNN allows fast and reliable removal of streaking artefact from under-sampled radial T 1 mapping images. Our results show that the highly non-linear operations of deep CNN processing of T 1 mapping images do not impact accurate reconstruction of myocardial T 1 maps.","journal":"Physics in Medicine and Biology","year":2020,"id":74095,"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":12,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9631,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":317787,"name":"Hossam El‐Rewaidy","orcid":"0000-0002-5266-8702","position":1,"is_corresponding":false},{"id":317790,"name":"Selçuk Küçükseymen","orcid":"0000-0002-9757-3088","position":2,"is_corresponding":false},{"id":388101,"name":"Thomas H. Hauser","orcid":"0000-0001-5960-8625","position":3,"is_corresponding":false},{"id":319230,"name":"Ahmed S. Fahmy","orcid":null,"position":4,"is_corresponding":false},{"id":388100,"name":"Maryam Nezafat","orcid":"0000-0002-2608-9977","position":0,"is_corresponding":true}],"reference_count":61,"raw_metadata":null,"created_at":"2026-07-18T21:45:52.171053Z","pmid":"33045693","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":[]}