3 citations · 6 across the 5 of their papers we have counts for
4 papers · 1 filter
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…