3 citations · 6 across the 5 of their papers we have counts for
5 papers
Enhance the Image: Super Resolution using Artificial Intelligence in MRI
Ziyu Li, Zihan Li, Haoxiang Li +5
This chapter provides an overview of deep learning techniques for improving the spatial resolution of MRI, ranging from convolutional neural networks, generative adversarial networ…
Improving accuracy and uncertainty quantification of deep learning based quantitative MRI using Monte Carlo dropout
Mehmet Yigit Avci, Ziyu Li, Qiuyun Fan +3
Dropout is conventionally used during the training phase as regularization method and for quantifying uncertainty in deep learning. We propose to use dropout during training as wel…
SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI
Qiyuan Tian, Ziyu Li, Qiuyun Fan +4
The noise in diffusion-weighted images (DWIs) decreases the accuracy and precision of diffusion tensor magnetic resonance imaging (DTI) derived microstructural parameters and leads…
SRDTI: Deep learning-based super-resolution for diffusion tensor MRI
Qiyuan Tian, Ziyu Li, Qiuyun Fan +8
High-resolution diffusion tensor imaging (DTI) is beneficial for probing tissue microstructure in fine neuroanatomical structures, but long scan times and limited signal-to-noise r…
Fast Sub-millimeter Diffusion MRI using gSlider-SMS and SNR-Enhancing Joint Reconstruction
Justin P. Haldar, Qiuyun Fan, Kawin Setsompop
We evaluate a new approach for achieving diffusion MRI data with high spatial resolution, large volume coverage, and fast acquisition speed. A recent method called gSlider-SMS enab…