8 citations · 9 across the 7 of their papers we have counts for
7 papers
Spectral Vision Transformer for Efficient Tokenization with Limited Data
Alexandra G. Roberts, Maneesh John, Jinwei Zhang +10
We propose a novel spectral vision transformer architecture for efficient tokenization in limited data, with an emphasis on medical imaging. We outline convenient theoretical prope…
Implicit neural representation for free-breathing MR fingerprinting (INR-MRF): co-registered 3D whole-liver water T1, water T2, proton density fat fraction, and R2* mapping
Chao Li, Jiahao Li, Jinwei Zhang +6
Purpose: To develop an MRI technique for free-breathing 3D whole-liver quantification of water T1, water T2, proton density fat fraction (PDFF), R2*. Methods: An Eight-echo spoiled…
MRI quantification of liver fibrosis using diamagnetic susceptibility: An ex-vivo feasibility study
Chao Li, Jinwei Zhang, Alexey V. Dimov +8
In chronic liver disease, liver fibrosis develops as excessive deposition of extracellular matrix macromolecules, predominantly collagens, progressively form fibrous scars that dis…
Deep learning improved autofocus for motion artifact reduction and its application in quantitative susceptibility mapping
Chao Li, Jinwei Zhang, Hang Zhang +4
Purpose: To develop a pipeline for motion artifact correction in mGRE and quantitative susceptibility mapping (QSM). Methods: Deep learning is integrated with autofocus to improve…
Multi-delay arterial spin-labeled perfusion estimation with biophysics simulation and deep learning
Renjiu Hu, Qihao Zhang, Pascal Spincemaille +2
Purpose: To develop biophysics-based method for estimating perfusion Q from arterial spin labeling (ASL) images using deep learning. Methods: A 3D U-Net (QTMnet) was trained to est…
Maximum Spherical Mean Value (mSMV) Filtering for Whole Brain Quantitative Susceptibility Mapping
Alexandra G. Roberts, Dominick J. Romano, Mert Şişman +6
To develop a tissue field filtering algorithm, called maximum Spherical Mean Value (mSMV), for reducing shadow artifacts in quantitative susceptibility mapping (QSM) of the brain w…
Deep Neural Network (DNN) for Water/Fat Separation: Supervised Training, Unsupervised Training, and No Training
R. Jafari, P. Spincemaille, J. Zhang +6
Purpose: To use a deep neural network (DNN) for solving the optimization problem of water/fat separation and to compare supervised and unsupervised training. Methods: The current T…