Most parallel imaging methods require calibration data for reconstruction. Low-rank-based methods allow calibration-free reconstruction from randomly undersampled, multi-channel data. This abstract presents a novel reconstruction method to combine multichannel blind deconvolution (MALBEC), a calibration-free method, and GRAPPA, a calibration-based method for highly accelerated imaging. The method sequentially performs MALBEC and GRAPPA with specially designed sampling masks such that the benefits of low-rank structure and the availability of calibration data can be utilized jointly. Our results demonstrate that the proposed method can achieve an acceleration factor that is the product of the factors achieved by MALBEC and GRAPPA alone.
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