Ting Liu
7 Papers
Ting Liu is an academic researcher. The author has contributed to research in topics: Residual & Computer science. The author has co-authored 1 publications.
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Papers
Deep robust residual network for super-resolution of 2D fetal brain MRI
TL;DR: Wang et al. as discussed by the authors proposed a robust residual-learning super-resolution network (RRLSRN) to generate a sharp HR brain image from an LR input, which combined the Charbonnier loss and GDL to improve the robustness of the model and enhance the texture information of SR results.
Deep robust residual network for super-resolution of 2D fetal brain MRI
TL;DR: Wang et al. as discussed by the authors proposed a robust residual-learning super-resolution network (RRLSRN) to generate a sharp HR brain image from an LR input, which combined the Charbonnier loss and GDL to improve the robustness of the model and enhance the texture information of SR results.
Difference of stiffness between the fetal and the maternal part of the placenta by virtual magnetic resonance elastography
Ting Liu,Jiaojiao Lu,Junjun Li,Jian Yang +3 more
TL;DR: Virtual Magnetic Resonance Elastography is to be used to study the difference in elasticity between the fetus and the maternal compartment to assess the elastography of the organization.
1
Intravoxel Incoherent Motion MR Imaging-based Virtual Elastography for the Assessment of Placenta Accreta
Jiaojiao Lu,Ting Liu,Junjun Li,Xinjun Li,Jian Yang +4 more
TL;DR: In this study, IVIM-based virtual elastography was used for the first time to detect the stiffness of the placenta, and it was found that the virtual (IVIM) stiffness values for the AP-ROIs were mostly higher than those for the NP- ROIs; the IR-ROI were also significantly higher than the NIR-ROIS.
1
Reference-based super-resolution with texture transformer and residual network
TL;DR: Zhang et al. as discussed by the authors proposed a super-resolution model based on texture transfer combined with residual network, which introduces perceptual loss, adversarial loss, reconstruction loss and texture loss to form a new loss function.