Zhen Zhao

TL;DR: Wang et al. as mentioned in this paper proposed a semi-supervised deep learning approach to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of the Wasserstein distance.

TL;DR: In this article, a semi-supervised deep learning approach was proposed to recover high-resolution (HR) CT images from low resolution (LR) counterparts by enforcing the cycle-consistency in terms of Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs.

TL;DR: This paper proposes a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the low-dose CT image quality, which incorporates3-D volumetric information to improved the image quality.

TL;DR: There was an association of older age and larger consolidation in upper lungs on admission with higher odds of poor outcomes in patients with COVID-19 pneumonia, and Multivariable logistic regression models were used to identify the risk factors.

TL;DR: Correlation analyses revealed that in patients, hypoperfusion in PCC and precuneus regions were related to higher insulin resistance level and deficits in clock-drawing performance, while the occipital hypoperFusion was associated with worse visual-memory performance, regardless of PVEs correction.