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

2020/04/03 by Elizabeth K. Cole, Cole, Elizabeth K., Joseph Y. Cheng +5
Engineering · Medicine · #Advanced MRI Techniques and Applications #Advanced X-ray and CT Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Physical sciences #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Medical Imaging Techniques and Applications #Medical Physics (physics.med-ph) #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2004.01738

openalex publication_date 2020/04/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

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

Citations

• Long Short-Term Memory
• Generalized autocalibrating partially parallel acquisitions (GRAPPA)
• Unitary Evolution Recurrent Neural Networks
• Associative Long Short-Term Memory
• Deep Complex Networks
• Unrolled Optimization with Deep Priors
• Complex Gated Recurrent Neural Networks
• Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data
• Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks

Related

• Complex Fully Convolutional Neural Networks for MR Image Reconstruction
• Co-VeGAN: Complex-Valued Generative Adversarial Network for Compressive Sensing MR Image Reconstruction
• Deep Learning-based MRI Reconstruction with Artificial Fourier Transform (AFT)-Net
• Better than Real: Complex-valued Neural Nets for MRI Fingerprinting
• Fully complex-valued deep learning model for visual perception