{"doi":"10.1002/mrm.28446","title":"RUN‐UP: Accelerated multishot diffusion‐weighted MRI reconstruction using an unrolled network with U‐Net as priors","abstract":"PURPOSE: To accelerate and improve multishot diffusion-weighted MRI reconstruction using deep learning. METHODS: An unrolled pipeline containing recurrences of model-based gradient updates and neural networks was introduced for accelerating multishot DWI reconstruction with shot-to-shot phase correction. The network was trained to predict results of jointly reconstructed multidirection data using single-direction data as input. In vivo brain and breast experiments were performed for evaluation. RESULTS: The proposed method achieves a reconstruction time of 0.1 second per image, over 100-fold faster than a shot locally low-rank reconstruction. The resultant image quality is comparable to the target from the joint reconstruction with a peak signal-to-noise ratio of 35.3 dB, a normalized root-mean-square error of 0.0177, and a structural similarity index of 0.944. The proposed method also improves upon the locally low-rank reconstruction (2.9 dB higher peak signal-to-noise ratio, 29% lower normalized root-mean-square error, and 0.037 higher structural similarity index). With training data from the brain, this method also generalizes well to breast diffusion-weighted imaging, and fine-tuning further reduces aliasing artifacts. CONCLUSION: A proposed data-driven approach enables almost real-time reconstruction with improved image quality, which improves the feasibility of multishot DWI in a wide range of clinical and neuroscientific studies.","journal":"Magnetic Resonance in Medicine","year":2020,"id":92180,"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":43,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9569,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"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":462509,"name":"Yunyingying Xu","orcid":null,"position":1,"is_corresponding":false},{"id":251970,"name":"Qiyuan Tian","orcid":"0000-0002-8350-5295","position":2,"is_corresponding":false},{"id":461267,"name":"Feiyu Chen","orcid":"0000-0002-2259-698X","position":3,"is_corresponding":false},{"id":461268,"name":"Xinwei Shi","orcid":"0000-0003-2303-0870","position":4,"is_corresponding":false},{"id":461269,"name":"Catherine J. Moran","orcid":"0000-0001-9748-9495","position":5,"is_corresponding":false},{"id":461270,"name":"Bruce L. Daniel","orcid":"0000-0003-0475-5892","position":6,"is_corresponding":false},{"id":301562,"name":"Brian A. Hargreaves","orcid":"0000-0003-0982-3508","position":7,"is_corresponding":false},{"id":263267,"name":"Yuxin Hu","orcid":"0000-0001-8045-9978","position":0,"is_corresponding":true}],"reference_count":52,"raw_metadata":null,"created_at":"2026-07-18T22:29:54.256205Z","pmid":"32783339","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":[]}