Paper Info
Title
Kernel Principal Component Analysis of Coil Compression in Parallel Imaging.
Abstract
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.
Year
DOI
Venue
2018
10.1155/2018/4254189
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Field
DocType
Volume
Computer vision,Compression (physics),Nonlinear system,Computer science,Communication channel,Phased array,Algorithm,Kernel principal component analysis,Electromagnetic coil,Acceleration,Artificial intelligence,Missing data
Journal
2018
ISSN
Citations 
PageRank 
1748-670X
0
0.34
References 
Authors
3
2
Name
Order
Citations
PageRank
Yuchou Chang119415.86
Haifeng Wang2215.87