Abstract
A new MR spectroscopic imaging method, called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation), has been recently proposed to enable high-resolution metabolic imaging with good SNR. A key problem within the SPICE framework is image reconstruction from a very noisy and sparsely sampled dataset. This paper addresses this problem by integrating the low-rank model used in SPICE reconstruction with a non-quadratic regularization. An efficient primal-dual based algorithm is described to solve the associated optimization problem. The proposed method has been validated using both simulation and phantom studies and is expected to enhance the unprecedented capability of SPICE for high-resolution metabolic imaging.
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