Ultrafast lumbar spine MRI protocol using deep learning–based reconstruction: diagnostic equivalence to a conventional protocol
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging
Link
https://link.springer.com/content/pdf/10.1007/s00256-022-04192-5.pdf
Reference34 articles.
1. Lockner JF, Hu HH, Stanley DW, Angelos L, King K. Parallel MR imaging: a user’s guide. Radiographics. 2005;25(5):1279–97. https://doi.org/10.1148/rg.255045202.
2. Jaspan ON, Fleysher R, Lipton ML. Compressed sensing MRI: a review of the clinical literature. Br J Radiol. 2015;88(1056):20150487. https://doi.org/10.1259/bjr.20150487.
3. Ji S, Yang D, Lee J, Choi SH, Kim H, Kang KM. Synthetic MRI: technologies and applications in neuroradiology. J Magn Reson Imaging. 2022;55(4):1013–25. https://doi.org/10.1002/jmri.27440.
4. Vargas MI, Drake-Pérez M, Delattre BMA, Boto J, Lovblad KO, Boudabous S. Feasibility of a synthetic MR imaging sequence for spine imaging. AJNR Am J Neuroradiol. 2018;39:1756–63.
5. Longo MG, Fagundes J, Huang S, et al. Simultaneous multislice-based 5-minute lumbar spine MRI protocol: initial experience in a clinical setting. J Neuroimaging. 2017;27(5):442–6.
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