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Impact of deep learning reconstruction on intracranial 1.5T magnetic resonance angiography. Jpn J Radiol 2022; 40:476\u2013483.","DOI":"10.1007\/s11604-021-01225-2"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9. Jeon YH, Park C, Lee KH, et al. Accelerated intracranial time-of-flight MR angiography with image-based deep learning image enhancement reduces scan times and improves image quality at 3-T and 1.5-T. Neuroradiology 2025; 67:1203\u20131213.","DOI":"10.1007\/s00234-025-03564-7"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10. Bash S, Wang L, Airriess C, et al. Deep learning enables 60% accelerated volumetric brain MRI while preserving quantitative performance: A prospective, multicenter, multireader trial. AJNR Am J Neuroradiol 2021; 42:2130\u20132137.","DOI":"10.3174\/ajnr.A7358"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11. Chen Z, Pawar K, Ekanayake M, Pain C, Zhong S, Egan GF. 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Performance characterization of a novel deep learning-based MR image reconstruction pipeline. arXiv preprint arXiv:2008.06559; 2020."},{"key":"16","unstructured":"16. King K, Xu D, Brau AC, Lai P, Beatty PJ, Marinelli L. A new combination of compressed sensing and data driven parallel imaging. Proceedings of the 18th Annual Meeting of ISMRM, Stockholm, 2010; 4881."},{"key":"17","doi-asserted-by":"crossref","unstructured":"17. Kijowski R, Rosas H, Samsonov A, King K, Peters R, Liu F. Knee imaging: Rapid three-dimensional fast spin-echo using compressed sensing. J Magn Reson Imaging 2017; 45:1712\u20131722.","DOI":"10.1002\/jmri.25507"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18. Pandit P, Rivoire J, King K, Li X. Accelerated T1\u03c1 acquisition for knee cartilage quantification using compressed sensing and data-driven parallel imaging: A feasibility study. 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