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

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.

References in corpus (8)

• Image reconstruction by domain transform manifold learning
• Unitary Evolution Recurrent Neural Networks
• Deep Complex Networks
• Unrolled Optimization with Deep Priors
• Associative Long Short-Term Memory
• Wasserstein GANs for MR Imaging: from Paired to Unpaired Training
• Highly Scalable Image Reconstruction using Deep Neural Networks with Bandpass Filtering
• Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data

Cited by in corpus (4)

• Unsupervised MRI Reconstruction with Generative Adversarial Networks
• Complex-valued Federated Learning with Differential Privacy and MRI Applications
• Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data
• BCNN: Binary Complex Neural Network