A new parallel imaging reconstruction framework is proposed to accelerate MRI using both coil sensitivity and data sparsity. This framework uses random undersampling and performs parallel imaging reconstruction with a k-space variant constraint. No calibration data are needed. It is demonstrated that this new approach offers a gain over conventional parallel imaging in imaging acceleration.
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