21 citations · 122 across the 25 of their papers we have counts for
25 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…
QSM-RimDS: A detection and segmentation tool for paramagnetic rim lesions in multiple sclerosis
Ha Luu, Mert Sisman, Ilhami Kovanlikaya +6
Paramagnetic rim lesions (PRLs) are an emerging biomarker in multiple sclerosis (MS). Manual identification and rim segmentation of PRLs on quantitative susceptibility mapping (QSM…
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…