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
Purpose We aim to acquire and reconstruct high signal-to-noise ratio (SNR), whole-brain, mesoscale (isotropic submillimeter) diffusion MRI (dMRI) in vivo in a clinically feasible scan time. Methods We present ROVER-dMRI, a unified rotating-view acquisition and super-resolution reconstruction framework. Thick slices (up...
Qiang Liu, Yun-Qi Wang, Yiang Pan et al.· bioRxiv· 0 citations
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
The proposed framework enables non-invasive Aβ mapping using a clinically feasible MRI protocol and may support repeated assessment for monitoring during anti-amyloid treatment.
Shohei Fujita, Y. Fushimi, Y. Otsuka et al.· Magnetic Resonance in Medica...· 0 citations
An open-source MRI workflow that harmonizes data acquisition, formatting, and reconstruction across sites and vendors is established, a critical step toward fully open and reproducible MRI, facilitating transparency, standardization, and collaboration across the research community.
Qing-Ping Chen, Thomas H. M. Roos, Amaya Murguia et al.· Magnetic Resonance in Medici...· 0 citations
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