Regularized Least Squares Estimating Sensitivity for Self-calibrating Parallel Imaging
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| Title | Regularized Least Squares Estimating Sensitivity for Self-calibrating Parallel Imaging |
| Authors | |
| 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. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2011-05-03 |
| Source | Journal of Computers Vol 6, No 5 (2011): Special Issue: Selected Best Papers of the International Workshop on CSEEE 2011 |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |