Keywords: Parallel Imaging, Parallel Imaging
Motivation: The reconstruction algorithms that rely on explicit sensitivity maps, like SENSE, are not robust, and prone to producing artifacts.
Goal(s): Our goal was to devise a novel, robust reconstruction method that is less susceptible to the inaccuracy of sensitivity maps.
Approach: The proposed method employs convolution kernels, derived from the singular value decomposition of block-wise Hankel matrix constructed from the k-space of sensitivity maps, to reconstruct under-sampled data.
Results: The proposed approach demonstrated superior and more robust performance compared to the SENSE method.
Impact: Transforming sensitivity maps into convolution kernels for parallel imaging can enhance image quality and robustness, and provide a novel perspective on leveraging sensitivity maps for researchers.
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