Meeting Banner
Abstract #4234

Kernelized Low-Rank: Improve Low-Rank with Adaptive Nonlinear Kernel for Dynamic MRI

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

Low-Rank 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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here