{"doi":"10.1002/mrm.29748","title":"Bloch simulator–driven deep recurrent neural network for magnetization transfer contrast <scp>MR</scp> fingerprinting and <scp>CEST</scp> imaging","abstract":"PURPOSE: To develop a unified deep-learning framework by combining an ultrafast Bloch simulator and a semisolid macromolecular magnetization transfer contrast (MTC) MR fingerprinting (MRF) reconstruction for estimation of MTC effects. METHODS: The Bloch simulator and MRF reconstruction architectures were designed with recurrent neural networks and convolutional neural networks, evaluated with numerical phantoms with known ground truths and cross-linked bovine serum albumin phantoms, and demonstrated in the brain of healthy volunteers at 3 T. In addition, the inherent magnetization-transfer ratio asymmetry effect was evaluated in MTC-MRF, CEST, and relayed nuclear Overhauser enhancement imaging. A test-retest study was performed to evaluate the repeatability of MTC parameters, CEST, and relayed nuclear Overhauser enhancement signals estimated by the unified deep-learning framework. RESULTS: Compared with a conventional Bloch simulation, the deep Bloch simulator for generation of the MTC-MRF dictionary or a training data set reduced the computation time by 181-fold, without compromising MRF profile accuracy. The recurrent neural network-based MRF reconstruction outperformed existing methods in terms of reconstruction accuracy and noise robustness. Using the proposed MTC-MRF framework for tissue-parameter quantification, the test-retest study showed a high degree of repeatability in which the coefficients of variance were less than 7% for all tissue parameters. CONCLUSION: Bloch simulator-driven, deep-learning MTC-MRF can provide robust and repeatable multiple-tissue parameter quantification in a clinically feasible scan time on a 3T scanner.","journal":"Magnetic Resonance in Medicine","year":2023,"id":325069,"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":32,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9617,"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":504731,"name":"Shanshan Jiang","orcid":"0000-0003-2853-9991","position":1,"is_corresponding":false},{"id":644424,"name":"Yuguo Li","orcid":"0000-0002-2281-4529","position":2,"is_corresponding":false},{"id":271396,"name":"Peter C.M. van Zijl","orcid":"0000-0002-9706-1066","position":3,"is_corresponding":false},{"id":504729,"name":"Jinyuan Zhou","orcid":"0000-0002-6684-7920","position":4,"is_corresponding":false},{"id":686908,"name":"Hye‐Young Heo","orcid":"0000-0002-7297-2015","position":5,"is_corresponding":false},{"id":1042003,"name":"Munendra Singh","orcid":"0000-0002-4988-6573","position":0,"is_corresponding":true}],"reference_count":67,"raw_metadata":null,"created_at":"2026-07-19T01:08:16.809511Z","pmid":"37317675","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":[]}