{"doi":"10.1002/mrm.29313","title":"<scp>Gradient‐echo‐train</scp>‐based sub‐millisecond periodic event encoded dynamic imaging with random (<i>k</i>, <i>t</i>)‐space undersampling: <i>k‐t</i><scp>get‐SPEEDI</scp>","abstract":"PURPOSE: The gradient-echo-train-based Sub-millisecond Periodic Event Encoded Dynamic Imaging (get-SPEEDI) technique provides ultrahigh temporal resolutions (∼0.6 ms) for detecting rapid physiological activities, but its practical adoption can be hampered by long scan times. This study aimed at developing a more efficient variant of get-SPEEDI for reducing the scan time without degrading temporal resolution or image quality. METHODS: The proposed pulse sequence, named k-t get-SPEEDI, accelerated get-SPEEDI acquisition by undersampling the k-space phase-encoding lines semi-randomly. At each time frame, k-space was fully sampled in the central region whereas randomly undersampled in the outer regions. A time-series of images was reconstructed using an algorithm based on the joint partial separability and sparsity constraints. To demonstrate the performance of k-t get-SPEEDI, images of human aortic valve opening and closing were acquired with 0.6-ms temporal resolution and compared with those from conventional get-SPEEDI. RESULTS: 70/38 vs. 73/39 in the k-t and conventional get-SPEEDI scans, respectively. The time courses of aortic valve area also matched well between these two sequences with a correlation coefficient of 0.86. CONCLUSIONS: The k-t get-SPEEDI pulse sequence was able to half the scan time without compromising the image quality and ultrahigh temporal resolution. Additional scan time reduction may also be possible, facilitating in vivo adoptions of SPEEDI techniques.","journal":"Magnetic Resonance in Medicine","year":2022,"id":295396,"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":1,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9462,"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":434362,"name":"Zheng Zhong","orcid":"0000-0002-0162-5964","position":1,"is_corresponding":false},{"id":434363,"name":"Kaibao Sun","orcid":"0000-0001-6923-5914","position":2,"is_corresponding":false},{"id":520698,"name":"Alessandro Scotti","orcid":"0000-0002-8892-881X","position":3,"is_corresponding":false},{"id":434365,"name":"Xiaohong Joe Zhou","orcid":"0000-0003-0793-4925","position":4,"is_corresponding":false},{"id":741129,"name":"Qingfei Luo","orcid":"0000-0001-5574-817X","position":0,"is_corresponding":true}],"reference_count":36,"raw_metadata":null,"created_at":"2026-07-19T00:31:08.861377Z","pmid":"35666824","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":[]}