Author

Zi-Han Li

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Open access 2026

SRNR: Deep learning-based MRI S uper- R esolution using N oisy high-resolution R eference data

Deep learning-based super-resolution (SR) techniques offer a powerful tool to enhance the spatial resolution of magnetic resonance imaging (MRI) without increasing scan time. However, most supervised SR methods rely on high-resolution, high signal-to-noise ratio (SNR) reference data for training, which are difficult...

Mu-Ye Zhang, Hong-Jia Yang, Zi-Han Li et al. · Imaging Neuroscience · 0 citations

Open access Sep 2026

Improving the accessibility of deep learning-based denoising for accelerated brain MRI using self-supervised learning and/or transfer learning.

The quality of human brain magnetic resonance imaging (MRI) is often compromised by noise. Deep learning-based image denoising methods using convolutional neural networks (CNNs) have proven to be more effective compared to conventional denoising methods. Nonetheless, most CNN-based denoising methods are not accessible...

Zi-Han Li, Zi-Yu Li, Ming-Xuan Liu et al. · NeuroImage · 0 citations

Zi-Han Li

SRNR: Deep learning-based MRI <b>S</b> uper- <b>R</b> esolution using <b>N</b> oisy high-resolution <b>R</b> eference data

Improving the accessibility of deep learning-based denoising for accelerated brain MRI using self-supervised learning and/or transfer learning.