Parallel imaging reconstruction using automatic regularization
✍ Scribed by Fa-Hsuan Lin; Kenneth K. Kwong; John W. Belliveau; Lawrence L. Wald
- Publisher
- John Wiley and Sons
- Year
- 2004
- Tongue
- English
- Weight
- 748 KB
- Volume
- 51
- Category
- Article
- ISSN
- 0740-3194
No coin nor oath required. For personal study only.
✦ Synopsis
Abstract
Increased spatiotemporal resolution in MRI can be achieved by the use of parallel acquisition strategies, which simultaneously sample reduced k‐space data using the information from multiple receivers to reconstruct full‐FOV images. The price for the increased spatiotemporal resolution in parallel MRI is the degradation of the signal‐to‐noise ratio (SNR) in the final reconstructed images. Part of the SNR reduction results when the spatially correlated nature of the information from the multiple receivers destabilizes the matrix inversion used in the reconstruction of the full‐FOV image. In this work, a reconstruction algorithm based on Tikhonov regularization is presented that reduces the SNR loss due to geometric correlations in the spatial information from the array coil elements. Reference scans are utilized as a priori information about the final reconstructed image to provide regularized estimates for the reconstruction using the L‐curve technique. This automatic regularization method reduces the average g‐factors in phantom images from a two‐channel array from 1.47 to 0.80 in twofold sensitivity encoding (SENSE) acceleration. In vivo anatomical images from an eight‐channel system show an averaged g‐factor reduction of 1.22 to 0.84 in 2.67‐fold acceleration. Magn Reson Med 51:559–567, 2004. © 2004 Wiley‐Liss, Inc.
📜 SIMILAR VOLUMES
## Abstract Parallel MRI techniques reconstruct full‐FOV images from undersampled __k‐__space data by using the uncorrelated information from RF array coil elements. One disadvantage of parallel MRI is that the image signal‐to‐noise ratio (SNR) is degraded because of the reduced data samples and th
## Abstract A new approach based on nonlinear inversion for autocalibrated parallel imaging with arbitrary sampling patterns is presented. By extending the iteratively regularized Gauss–Newton method with variational penalties, the improved reconstruction quality obtained from joint estimation of i
## Abstract A new reconstruction method for parallel MRI called PROBER is proposed. The method PROBER works in an image domain similar to methods based on Sensitivity Encoding (SENSE). However, unlike SENSE, which first estimates the spatial sensitivity maps, PROBER approximates the reconstruction