CLEAR: Calibration-Free Parallel Imaging Using Locally Low-Rank Encouraging Reconstruction

Joshua D. Trzasko¹, Armando Manduca¹

In this work, we present a calibration-free locally low-rank encouraging reconstruction (CLEAR) strategy for accelerated parallel imaging applications. Whereas existing calibrationless parallel MRI methods operate entirely in k-space, using globally-constrained reconstruction models, our proposed strategy imposes constraints locally in the image domain. As we demonstrate, this approach offers substantial computational advantage, is very amenable to parallelized implementation, and naturally incorporates with Compressive Sensing-type sparsity constraints.