A Deep Recursive Cascaded Convolutional Network for Parallel MRI

doi:10.11938/cjmr20192721

Abstract

Abstract: Fast magnetic resonance imaging (MRI) has been attracting more and more research interests in recent years. With the emergence of big data and development of advanced deep learning algorithms, neural network has become a common and effective tool for image reconstruction in fast MRI. One main challenge to the deep learning-based methods for fast MRI reconstruction is the trade-off between the network performance and the network capacity. Few previous studies have used the deep learning-based methods in parallel imaging. In this work, a deep recursive cascaded convolutional network (DRCCN) architecture was designed for parallel MRI, with reduced number of network parameters while maintaining a satisfactory performance. The experimental results demonstrated that, compared to the classical methods, image reconstruction with the well-trained DRCCN networks were more accurate and less time consuming.

Key words: fast magnetic resonance imaging, parallel imaging, deep learning, convolutional neural network, prior knowledge

CLC Number:

O482.53

CHENG Hui-tao, WANG Shan-shan, KE Zi-wen, JIA Sen, CHENG Jing, QIU Zhi-lang, ZHENG Hai-rong, LIANG Dong. A Deep Recursive Cascaded Convolutional Network for Parallel MRI[J]. Chinese Journal of Magnetic Resonance, 2019, 36(4): 437-445.

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