Analysis of deep complex-valued convolutional neural networks for MRI reconstruction and phase-focused applications.
PURPOSE
Deep learning has had success with MRI reconstruction, but previously published works use real-valued networks. The few works which have tried complex-valued networks have not fully assessed their impact on phase. Therefore, the purpose of this work is to fully investigate end-to-end complex-valued convolutional neural networks (CNNs) for accelerated MRI reconstruction and in several phase-based applications in comparison to 2-channel real-valued networks.


METHODS
Several complex-valued activation functions for MRI reconstruction were implemented, and their performance was compared. Complex-valued convolution was implemented and tested on an unrolled network architecture and a U-Net-based architecture over a wide range of network widths and depths with knee, body, and phase-contrast datasets.


RESULTS
Quantitative and qualitative results demonstrated that complex-valued CNNs with complex-valued convolutions provided superior reconstructions compared to real-valued convolutions with the same number of trainable parameters for both an unrolled network architecture and a U-Net-based architecture, and for 3 different datasets. Complex-valued CNNs consistently had superior normalized RMS error, structural similarity index, and peak SNR compared to real-valued CNNs.


CONCLUSION
Complex-valued CNNs can enable superior accelerated MRI reconstruction and phase-based applications such as fat-water separation, and flow quantification compared to real-valued convolutional neural networks.
['Cole|Elizabeth|E|0000-0001-6123-2495', 'Cheng|Joseph|J|', 'Pauly|John|J|', 'Vasanawala|Shreyas|S|']
D000465:Algorithms D006801:Humans D007091:Image Processing, Computer-Assisted D007717:Knee D008279:Magnetic Resonance Imaging D016571:Neural Networks, Computer
convolutional neural networks MRI complex-valued models image reconstruction learning representations