3 citations · 3 across the 5 of their papers we have counts for
5 papers
Model-based T1, T2* and Proton Density Mapping Using a Bayesian Approach with Parameter Estimation and Complementary Undersampling Patterns
Shuai Huang, James J. Lah, Jason W. Allen +1
Purpose: To achieve automatic hyperparameter estimation for the joint recovery of quantitative MR images, we propose a Bayesian formulation of the reconstruction problem that incor…
Robust Quantitative Susceptibility Mapping via Approximate Message Passing with Parameter Estimation
Shuai Huang, James J. Lah, Jason W. Allen +1
Purpose: For quantitative susceptibility mapping (QSM), the lack of ground-truth in clinical settings makes it challenging to determine suitable parameters for the dipole inversion…
A Probabilistic Bayesian Approach to Recover map and Phase Images for Quantitative Susceptibility Mapping
Shuai Huang, James J. Lah, Jason W. Allen +1
Purpose: Undersampling is used to reduce the scan time for high-resolution 3D magnetic resonance imaging. In order to achieve better image quality and avoid manual parameter tuning…
Fast Nonconvex Mapping Using ADMM
Shuai Huang, James J. Lah, Jason W. Allen +1
Magnetic resonance (MR)- mapping is widely used to study hemorrhage, calcification and iron deposition in various clinical applications, it provides a direct and precise map…
SAGE: Sequential Attribute Generator for Analyzing Glioblastomas using Limited Dataset
Padmaja Jonnalagedda, Brent Weinberg, Jason Allen +3
While deep learning approaches have shown remarkable performance in many imaging tasks, most of these methods rely on availability of large quantities of data. Medical image data,…