Abstract #3699
PRAIRIE: Accelerating MR Parameter Mapping Using Kernel-Based Manifold Learning and Pre-Imaging
Yihang Zhou 1 , Chao Shi 1 , Yanhua Wang 1 , Jingyuan Lyu 1 , and Leslie Ying 1,2
1
Department of Electrical Engineering, State
University of New York at Buffalo, Buffalo, NY, United
States,
2
Department
of Biomedical Engineering, State University of New York
at Buffalo, Buffalo, NY, United States
In this study, a novel reconstruction method using
kernel-based manifold learning and regularized
pre-imaging is proposed to accelerate the MR parameter
mapping. The parametric-weighted image at a specific
time point is assumed to lie in a low-dimensional
manifold and is reconstructed individually. The
low-dimensional manifold is learned from the training
images generated by the parametric model. The underlying
optimization problem is solved using kernel trick and
split Bregman iteration algorithm. Our preliminary
result demonstrated that the proposed method is able to
accurately recover the T2 map at high reduction factors
when the conventional compressed sensing methods with
linear models fail.
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