{"doi":"10.1002/mrm.29677","title":"Time‐dependent diffusion <scp>MRI</scp> using multiple stimulated echoes","abstract":"Purpose To develop a time‐efficient pulse sequence that acquires multiple diffusion‐weighted images with distinct diffusion times in a single shot by using multiple stimulated echoes (mSTE) with variable flip angles (VFA). Methods The proposed diffusion‐weighted mSTE with VFA (DW‐mSTE‐VFA) sequence begins with two 90° RF pulses that straddle a diffusion gradient lobe ( G D ) to excite and restore one half of the magnetization into the longitudinal axis. The restored longitudinal magnetization was successively re‐excited by a series of RF pulses with VFA, each followed by another G D , to generate a set of stimulated echoes. Each of the multiple stimulated echoes was acquired with an EPI echo train. As such, the train of multiple stimulated echoes produced a set of diffusion‐weighted images with varying diffusion times in a single shot. This technique was experimentally demonstrated on a diffusion phantom, a fruit, and healthy human brain and prostate at 3 T. Results In the phantom experiment, the mean ADC measured at different diffusion times using DW‐mSTE‐VFA were highly consistent (r = 0.999) with those from a commercial spin‐echo diffusion‐weighted EPI sequence. In the fruit and brain experiments, DW‐mSTE‐VFA exhibited similar diffusion‐time dependence to a standard diffusion‐weighted stimulated echo sequence. The ADC showed significant time dependence in the human brain ( p = 0.003 in both white matter and gray matter) and prostate tissues ( p = 0.003 in both peripheral zone and central gland). Conclusion DW‐mSTE‐VFA offers a time‐efficient tool for investigating the diffusion‐time dependency in diffusion MRI studies.","journal":"Magnetic Resonance in Medicine","year":2023,"id":374668,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9603,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":434363,"name":"Kaibao Sun","orcid":"0000-0001-6923-5914","position":1,"is_corresponding":false},{"id":741129,"name":"Qingfei Luo","orcid":"0000-0001-5574-817X","position":2,"is_corresponding":false},{"id":434365,"name":"Xiaohong Joe Zhou","orcid":"0000-0003-0793-4925","position":3,"is_corresponding":false},{"id":741128,"name":"Guangyu Dan","orcid":"0000-0003-0427-5661","position":0,"is_corresponding":true}],"reference_count":41,"raw_metadata":null,"created_at":"2026-07-19T01:16:15.380731Z","pmid":"37103885","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":[]}