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.

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