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Abstract #1166

Coil Sensitivity Estimation with Deep Sets Towards End-to-End Accelerated MRI Reconstruction

Mahmoud Mostapha1, Boris Mailhe1, Simon Arberet1, Dominik Nickel2, and Mariappan S. Nadar 1
1Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States, 2Magnetic Resonance, Siemens Healthineers, Erlangen, Germany

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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