Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI
Author:
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,Radiological and Ultrasound Technology,Biophysics
Link
https://link.springer.com/content/pdf/10.1007/s10334-023-01127-6.pdf
Reference79 articles.
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3. Ueda T, Ohno Y, Yamamoto K, Iwase A, Fukuba T, Hanamatsu S, Obama Y, Ikeda H, Ikedo M, Yui M, Murayama K, Toyama H (2021) Compressed sensing and deep learning reconstruction for women’s pelvic MRI denoising: utility for improving image quality and examination time in routine clinical practice. Eur J Radiol 134:109430
4. Do W, Seo S, Han Y, Ye JC, Choi SH, Park S (2020) Reconstruction of multicontrast MR images through deep learning. Med Phys 47:983–997
5. Sandino CM, Cheng JY, Chen F, Mardani M, Pauly JM, Vasanawala SS (2020) Compressed sensing: from research to clinical practice with deep neural networks: shortening scan times for magnetic resonance imaging. IEEE Signal Process Mag 37:117–127
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