1. A Deep Recursive Cascaded Convolutional Network for Parallel MRI
- Author
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CHENG Hui-tao, WANG Shan-shan, KE Zi-wen, JIA Sen, CHENG Jing, QIU Zhi-lang, ZHENG Hai-rong, and LIANG Dong
- Subjects
lcsh:QC501-766 ,prior knowledge ,fast magnetic resonance imaging ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,lcsh:Electricity and magnetism ,deep learning ,convolutional neural network ,parallel imaging - 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.
- Published
- 2019