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

• URL: http://arxiv.org/abs/2004.01738v4
• Date: Tue, 12 May 2020 01:21:36 GMT
• Title: Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction
• Authors: Elizabeth K. Cole, Joseph Y. Cheng, John M. Pauly, and Shreyas S. Vasanawala
• Abstract summary: We investigate end-to-end complex-valued convolutional neural networks for image reconstruction in lieu of two-channel real-valued networks. 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.
• Score: 9.55767753037496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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