Fast Calibrationless Image-space Reconstruction by Structured Low-rank Tensor Estimation of Coil Sensitivity and Spatial Support

Abstract

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.