Keywords: Parallel Imaging, Sparse & Low-Rank ModelsSensitivity map estimation is important in many multichannel MRI applications. Subspace-based sensitivity map estimation methods like ESPIRiT are popular and perform well, though can be computationally expensive and their theoretical principles can be nontrivial to understand. In this work, we derive a new theoretical framework for sensitivity map estimation from a structured low-rank modeling perspective. This results in an estimation approach that is equivalent to ESPIRiT, but with theory that may be more intuitive for some readers. In addition, we propose a set of computational acceleration techniques that enable substantial (~25-fold) improvements in computational time for subspace-based sensitivity map estimation.
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