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Computational and Mathematical Methods in Medicine
Volume 2017 (2017), Article ID 7685208, 9 pages
https://doi.org/10.1155/2017/7685208
Research Article

A Feasibility Study of Geometric-Decomposition Coil Compression in MRI Radial Acquisitions

1Department of Biomedical Engineering, Zhejiang University, Hangzhou 310027, China
2Center for Brain Imaging Science and Technology, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China
3State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China

Correspondence should be addressed to Ling Xia; nc.ude.ujz@gnilaix

Received 6 January 2017; Accepted 10 May 2017; Published 4 June 2017

Academic Editor: Chuangyin Dang

Copyright © 2017 Jing Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Receiver arrays with a large number of coil elements are becoming progressively available because of their increased signal-to-noise ratio (SNR) and enhanced parallel imaging performance. However, longer reconstruction time and intensive computational cost have become significant concerns as the number of channels increases, especially in some iterative reconstructions. Coil compression can effectively solve this problem by linearly combining the raw data from multiple coils into fewer virtual coils. In this work, geometric-decomposition coil compression (GCC) is applied to radial sampling (both linear-angle and golden-angle patterns are discussed) for better compression. GCC, which is different from directly compressing in -space, is performed separately in each spatial location along the fully sampled directions, then followed by an additional alignment step to guarantee the smoothness of the virtual coil sensitivities. Both numerical simulation data and in vivo data were tested. Experimental results demonstrated that the GCC algorithm can achieve higher SNR and lower normalized root mean squared error values than the conventional principal component analysis approach in radial acquisitions.