Coil compression is performed in magnetic resonance imaging (MRI) to enable smaller datasets and faster computation time. However, the traditional coil compression process is lengthy and lossy. In this work, we proposed a novel neural network-based coil compression method to achieve higher reconstruction accuracy and faster coil compression. Our method consistently achieved up to 1.5x lower NRMSE compared to SVD and GCC on the fastMRI knee dataset. The computational requirements of our method are practical, and inference runs 10 times faster than the traditional methods.
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