JCP 2011 Vol.6(5): 857-864 ISSN: 1796-203X
doi: 10.4304/jcp.6.5.857-864
doi: 10.4304/jcp.6.5.857-864
Regularized Least Squares Estimating Sensitivity for Self-calibrating Parallel Imaging
XiaoFang Liu1, Xiuzi Ye2, Sanyuan Zhang1, Feng Liu3
1College of Computer Science and Technology, Zhejiang University, HangZhou, China, Department of Biomedical Engineering, China Jiliang University, HangZhou, China
2College of Computer Science and Technology, Zhejiang University, HangZhou, China
3School of Information Technology & Electrical Engineering,The University of Queensland, Brisbane, Australia
Abstract—Calibration of the spatial sensitivity functions of coil arrays is a crucial element in parallel magnetic resonance imaging (pMRI). The self-calibrating technique for sensitivity extraction has complemented the common calibration technique that uses a separate pre-scan. In order to improve the accuracy of sensitivity estimate from small number of self-calibrating data, which is extracted from a fully sampled central region of a variable-density k-space acquisition in self-calibrating parallel images, a novel scheme for estimating the sensitivity profiles is proposed in the paper. On consideration of truncation error and measurement errors in self-calibrating data, the issue of calculating sensitivity would be formulated as a regularized least squares estimation problem, which is solved by the preconditioned conjugate gradients algorithm. When applying the estimated coil sensitivity to reconstruct full field-of-view(FOV) image from the under-sampling simulated and in vivo data, the normalized signal-to-noise ratio (NSNR) of reconstruction image is evidently improved, and meanwhile the normalized mean squared error (NMSE) is remarkably reduced, especially when a rather large accelerate factor is used.
Index Terms—Parallel magnetic resonance imaging (pMRI), self-calibrating technique, regularized least squares (RLS), preconditioned conjugate gradients (PCG), generalized encoding matrix(GEM) reconstruction
2College of Computer Science and Technology, Zhejiang University, HangZhou, China
3School of Information Technology & Electrical Engineering,The University of Queensland, Brisbane, Australia
Abstract—Calibration of the spatial sensitivity functions of coil arrays is a crucial element in parallel magnetic resonance imaging (pMRI). The self-calibrating technique for sensitivity extraction has complemented the common calibration technique that uses a separate pre-scan. In order to improve the accuracy of sensitivity estimate from small number of self-calibrating data, which is extracted from a fully sampled central region of a variable-density k-space acquisition in self-calibrating parallel images, a novel scheme for estimating the sensitivity profiles is proposed in the paper. On consideration of truncation error and measurement errors in self-calibrating data, the issue of calculating sensitivity would be formulated as a regularized least squares estimation problem, which is solved by the preconditioned conjugate gradients algorithm. When applying the estimated coil sensitivity to reconstruct full field-of-view(FOV) image from the under-sampling simulated and in vivo data, the normalized signal-to-noise ratio (NSNR) of reconstruction image is evidently improved, and meanwhile the normalized mean squared error (NMSE) is remarkably reduced, especially when a rather large accelerate factor is used.
Index Terms—Parallel magnetic resonance imaging (pMRI), self-calibrating technique, regularized least squares (RLS), preconditioned conjugate gradients (PCG), generalized encoding matrix(GEM) reconstruction
Cite: XiaoFang Liu, Xiuzi Ye, Sanyuan Zhang, Feng Liu , "Regularized Least Squares Estimating Sensitivity for Self-calibrating Parallel Imaging," Journal of Computers vol. 6, no. 5, pp. 857-864, 2011.
General Information
ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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