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It develops a simulation-driven approach using complex-valued multi-layer perceptrons and convolutional neural networks to efficiently process MRF data, enabling generalization across sequence and acquisition parameters and eliminating the need for extensive in vivo training data. Evaluation on simulated and in vivo data showed that MRF-Mixer outperforms dictionary matching and existing deep learning methods for T1 and T2 mapping. In six-shot simulations, it achieved the highest PSNR (T1: 33.48, T2: 35.9) and SSIM (T1: 0.98, T2: 0.98) and the lowest MAE (T1: 28.8, T2: 4.97) and RMSE (T1: 72.9, T2: 13.67). In vivo results further demonstrate that single-shot reconstructions using MRF-Mixer matched the quality of multi-shot acquisitions, highlighting its potential to reduce scan times. These findings suggest that MRF-Mixer enables faster, more accurate multiparametric tissue mapping, substantially improving quantitative MRI for clinical applications by reducing acquisition time while maintaining imaging quality.<\/jats:p>","DOI":"10.3390\/info16030218","type":"journal-article","created":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T08:59:52Z","timestamp":1741683592000},"page":"218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting Reconstruction"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6483-4093","authenticated-orcid":false,"given":"Tianyi","family":"Ding","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3846-0999","authenticated-orcid":false,"given":"Yang","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5518-0681","authenticated-orcid":false,"given":"Zhuang","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0636-234X","authenticated-orcid":false,"given":"Martijn A.","family":"Cloos","sequence":"additional","affiliation":[{"name":"Donders Centre for Cognitive Neuroimaging, Radboud University, 6525 Nijmegen, The Netherlands"},{"name":"Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3436-7831","authenticated-orcid":false,"given":"Hongfu","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, QLD 4072, Australia"},{"name":"School of Engineering, University of Newcastle, Callaghan, NSW 2308, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20201215","DOI":"10.1259\/bjr.20201215","article-title":"Clinical quantitative MRI and the need for metrology","volume":"94","author":"Cashmore","year":"2021","journal-title":"Br. 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