Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial IntelligenceComplex-valued CNN based image reconstruction methods have been proposed to correspond to MR images with a spatial phase variation. However, using those CNN may lead to over-fitting because CNN layers for complex numbers are requires large number of parameters than real-valued CNN. We previously proposed a reconstruction method for complex-valued image using a real-valued DnCNN by introducing a symmetrical k-space under-sampling. In this study, we introduced this method to U-Net and ADMM-CSNet. Reconstruction experiments showed that a real-valued CNN has the possibility to have the same or better performance as a complex-valued CNN without perform complex calculations.
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