MR-self Noise2Noise: self-supervised deep learning–based image quality improvement of submillimeter resolution 3D MR images
Author:
Funder
Ministry of Science and ICT, South Korea
Publisher
Springer Science and Business Media LLC
Subject
Radiology, Nuclear Medicine and imaging,General Medicine
Link
https://link.springer.com/content/pdf/10.1007/s00330-022-09243-y.pdf
Reference40 articles.
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3. Noh Y, Sung YH, Lee J, Kim EY (2015) Nigrosome 1 detection at 3T MRI for the diagnosis of early-stage idiopathic parkinson disease: assessment of diagnostic accuracy and agreement on imaging asymmetry and clinical laterality. AJNR Am J Neuroradiol 36:2010–2016. https://doi.org/10.3174/ajnr.a4412
4. Buades A, Coll B, Morel JM (2005) A review of image denoising algorithms, with a new one. Multiscale Model Simul 4:490–530. https://doi.org/10.1137/040616024
5. Zhang K, Zuo W, Chen Y et al (2017) Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans Image Process 26:3142–3155. https://doi.org/10.1109/tip.2017.2662206
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