Analysis of Deep Complex-Valued Convolutional Neural Networks for MRI Reconstruction
This addresses the need for faster MRI scans by leveraging the inherent complex-valued nature of the data, though it is incremental as it builds on existing CNN methods.
The paper tackled the problem of MRI reconstruction by using complex-valued convolutional neural networks instead of real-valued ones, finding that they provide superior reconstructions across various architectures and datasets.
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.