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Abstract

Methods are described for efficient reconstruction of MRI data. In one practice, new reconstruction algorithms for non-uniformly sampled k-space data are presented. In the disclosed algorithms, Iterative Next-Neighbor re-Gridding (INNG) and Block INNG (BINNG), iterative procedures are performed using larger rescaled matrices than the target grid matrix In BINNG algorithm, the sampled k-space region is partitioned into several blocks and the INNG algorithm is applied to each block. In another practice, a novel partial spiral reconstruction (PFSR) uses an estimated phase map from a low-resolution image reconstructed from the central k-space data and performs iterations, similar to the iterative procedures with INNG, with an imposed phase constraint. According to yet another practice, an off-resonance correction is performed on matrices that are smaller than the full image matrix. All these methods reduce the computational costs while rendering high-quality reconstructed images.

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