In conventional parallel imaging, coil sensitivity information can be obtained from calibration data for reconstruction that inevitably prolongs MRI scan. In recent years, structured low-rank matrix completion methods implicitly exploit coil sensitivity that enables calibrationless k-space estimation while prohibitively increases the computational burden. This study presents a fast and calibrationless image-space alternative for reconstruction that derives high-quality coil sensitivity and spatial support maps by structured low-rank tensor estimation. The proposed approach was evaluated with multi-channel multi-contrast brain datasets. It achieves a high convergence rate with significantly reduced reconstruction time, making the calibrationless reconstruction approach more efficient in clinical practice.
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