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arXiv · 2004.01738

Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction

Elizabeth K. Cole·Joseph Y. Cheng·John M. Pauly·Shreyas S. Vasanawala
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Abstract

Many real-world signal sources are complex-valued, having real and imaginary components. However, the vast majority of existing deep learning platforms and network architectures do not support the use of complex-valued data. MRI data is inherently complex-valued, so existing approaches discard the richer algebraic structure of the complex data. In this work, we investigate end-to-end complex-valued convolutional neural networks - specifically, for image reconstruction in lieu of two-channel real-valued networks. We apply this to magnetic resonance imaging reconstruction for the purpose of accelerating scan times and determine the performance of various promising complex-valued activation functions. We find that complex-valued CNNs with complex-valued convolutions provide superior reconstructions compared to real-valued convolutions with the same number of trainable parameters, over a variety of network architectures and datasets.

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Elizabeth K. Cole, Joseph Y. Cheng, John M. Pauly, Shreyas S. Vasanawala. 2020-05-12. Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction. https://arxiv.org/abs/2004.01738

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