Accurate estimation of coil sensitivity functions is essential for SENSE-based image reconstruction. This paper presents a new method for joint estimation of coil sensitivity functions and image reconstruction from sparsely sampled k-space data without any auto-calibration data. The proposed method uses a probabilistic subspace model to capture statistical spatial distributions of a given coil system from prior scan data. The proposed method has been validated using experimental data (fastMRI dataset), producing high-quality reconstruction results from calibrationless sparse data (acceleration factor R = 8 or R = 16). The proposed method may further enhance the practical utility of parallel imaging.
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