中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Kernel Principal Component Analysis of Coil Compression in Parallel Imaging.

文献类型:期刊论文

作者Chang, Yuchou; Wang, Haifeng
刊名COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
出版日期2018
文献子类期刊论文
英文摘要A phased array with many coil elements has been widely used in parallel MRI for imaging acceleration. On the other hand, it results in increased memory usage and large computational costs for reconstructing the missing data from such a large number of channels. A number of techniques have been developed to linearly combine physical channels to produce fewer compressed virtual channels for reconstruction. A new channel compression technique via kernel principal component analysis (KPCA) is proposed. Theproposed KPCA method uses a nonlinear combination of all physical channels to produce a set of compressed virtual channels. This method not only reduces the computational time but also improves the reconstruction quality of all channels when used. Taking the traditional GRAPPA algorithmas an example, it is shown that the proposed KPCA method can achieve better quality than both PCA and all channels, and at the same time the calculation time is almost the same as the existing PCA method.
URL标识查看原文
语种英语
源URL[http://ir.siat.ac.cn:8080/handle/172644/14329]  
专题深圳先进技术研究院_医工所
推荐引用方式
GB/T 7714
Chang, Yuchou,Wang, Haifeng. Kernel Principal Component Analysis of Coil Compression in Parallel Imaging.[J]. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE,2018.
APA Chang, Yuchou,&Wang, Haifeng.(2018).Kernel Principal Component Analysis of Coil Compression in Parallel Imaging..COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE.
MLA Chang, Yuchou,et al."Kernel Principal Component Analysis of Coil Compression in Parallel Imaging.".COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE (2018).

入库方式: OAI收割

来源:深圳先进技术研究院

浏览0
下载0
收藏0
其他版本

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。