Analysis of deep complex‐valued convolutional neural networks for MRI reconstruction and phase‐focused applications

卷积神经网络 计算机科学 卷积(计算机科学) 人工智能 深度学习 模式识别(心理学) 相似性(几何) 相(物质) 复杂网络 建筑 算法 网络体系结构 人工神经网络 图像(数学) 物理 量子力学 万维网 艺术 计算机安全 视觉艺术
作者
Elizabeth K. Cole,Joseph Y. Cheng,John M. Pauly,Shreyas Vasanawala
出处
期刊:Magnetic Resonance in Medicine [Wiley]
卷期号:86 (2): 1093-1109 被引量:100
标识
DOI:10.1002/mrm.28733
摘要

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.
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