3 citations · 6 across the 5 of their papers we have counts for
5 papers
SRNR: Training neural networks for Super-Resolution MRI using Noisy high-resolution Reference data
Jiaxin Xiao, Zihan Li, Berkin Bilgic +3
Neural network (NN) based approaches for super-resolution MRI typically require high-SNR high-resolution reference data acquired in many subjects, which is time consuming and a bar…
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…
Ground-truth resting-state signal provides data-driven estimation and correction for scanner distortion of fMRI time-series dynamics
Rajat Kumar, Liang Tan, Alan Kriegstein +4
The fMRI community has made great strides in decoupling neuronal activity from other physiologically induced T2* changes, using sensors that provide a ground-truth with respect to…
Highly Accelerated Multishot EPI through Synergistic Machine Learning and Joint Reconstruction
Berkin Bilgic, Itthi Chatnuntawech, Mary Kate Manhard +7
Purpose: To introduce a combined machine learning (ML) and physics-based image reconstruction framework that enables navigator-free, highly accelerated multishot echo planar imagin…