Complex-valued residual network learning for parallel MR imaging
文献类型：会议论文
| 作者 | Shanshan Wang; Huitao Cheng; Ziwen Ke; Leslie Ying; Xin liu; Hairong Zheng; Dong Liang |
| 出版日期 | 2018 |
| 会议日期 | 2018 |
| 会议地点 | 法国巴黎 |
| 英文摘要 | Applying deep learning to fast MR imaging has been new and highly evolved. This direction utilizes networks to draw valuable prior information from available big datasets and then assists fast online imaging. Nevertheless, most existing works adopt real-valued network structures while MR images are complex-valued. This paper proposes a complex-valued residual network learning framework for parallel MR imaging. Specifically, complex-valued convolution and initialization strategy are provided. Residual connections are also adopted to learn a more accurate prior. Experimental results show that the proposed method could achieve improved complex-valued image reconstruction with much less time compared to GRAPPA and SPIRiT. |
| URL标识 | 查看原文 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/14540]  |
| 专题 | 深圳先进技术研究院_医工所 |
| 推荐引用方式 GB/T 7714 | Shanshan Wang,Huitao Cheng,Ziwen Ke,et al. Complex-valued residual network learning for parallel MR imaging[C]. 见:. 法国巴黎. 2018. |
入库方式： OAI收割
来源：深圳先进技术研究院
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