Enhao Gong1, Tao Zhang1, Joseph Cheng1, Shreyas Vasanawala2, and John Pauly1
1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States
methods are widely applied to improve reconstruction for Dynamic Contrast
Enhanced (DCE) MRI by imposing linear spatial-temporal correlation in
global, local or multiple scales. This assumption does not fully capture the highly
nonlinear spatial-temporal dynamics of DCE signals. We proposed a generalized
Kernelized-Low-Rank model, assumed Low-Rank property after nonlinear transform and
solved it by Regularizing singular-values with Adaptive Nonlinear Kernels. The
proposed method captures the spatial-temporal dynamics as a sparser
representation and achieves more accurate reconstruction results.
Kernelized-Low-Rank model can be easily integrated to provide
significant improvements to Global Low-Rank, Locally Low-Rank, LR+S and
Multi-scale LR models.