3 citations · 6 across the 5 of their papers we have counts for
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eess.IV2021★ 3 cited
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…
eess.IV2021
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…
eess.IV2021★ 3 cited
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…