Parallel Imaging (PI) is a crucial technique for accelerating data acquisition in Magnetic Resonance Imaging (MRI), which is exceedingly time-consuming. With current SENSE-based MRI reconstruction formulated as a trainable unrolled optimization framework with several cascades of regularization networks and varying data consistency layers, coils sensitivity maps (CSMs) are needed at each cascade. Therefore, we propose a deep sets CSM estimation network (DS-CSME in short), enabling an end-to-end deep learning solution that allows for further MRI acceleration while preserving the overall reconstructed image quality.
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