To improve MRI reconstruction accuracy, we propose various complex-valued frameworks for reconstructions using convolutional neural networks. By introducing complex-valued convolution and activation functions, we improve reconstruction of our subsampled images and achieve competitive results compared to the real-valued counterpart of our model.
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