8 citations · 18 across the 5 of their papers we have counts for
5 papers
Harmonization in Magnetic Resonance Imaging: A Survey of Acquisition, Image-level, and Feature-level Methods
Qinqin Yang, Firoozeh Shomal-Zadeh, Ali Gholipour
Magnetic resonance imaging (MRI) has greatly advanced neuroscience research and clinical diagnostics. However, imaging data collected across different scanners, acquisition protoco…
FlexDTI: Flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning
Zejun Wu, Jiechao Wang, Zunquan Chen +9
Objective: Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in…
High-efficient Bloch simulation of magnetic resonance imaging sequences based on deep learning
Haitao Huang, Qinqin Yang, Jiechao Wang +3
Objective: Bloch simulation constitutes an essential part of magnetic resonance imaging (MRI) development. However, even with the graphics processing unit (GPU) acceleration, the h…
Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance
Qinqin Yang, Zi Wang, Kunyuan Guo +2
Deep learning has innovated the field of computational imaging. One of its bottlenecks is unavailable or insufficient training data. This article reviews an emerging paradigm, imag…
Model-based Synthetic Data-driven Learning (MOST-DL): Application in Single-shot T2 Mapping with Severe Head Motion Using Overlapping-echo Acquisition
Qinqin Yang, Yanhong Lin, Jiechao Wang +12
Use of synthetic data has provided a potential solution for addressing unavailable or insufficient training samples in deep learning-based magnetic resonance imaging (MRI). However…