Kernel Principal Component Analysis of Coil Compression in Parallel Imaging.

	Kernel Principal Component Analysis of Coil Compression in Parallel Imaging.
	Chang, Yuchou; Wang, Haifeng
刊名	COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
	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
内容类型	期刊论文
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).