MR image sparsity/compressibility has been widely exploited for imaging acceleration with the development of compressed sensing. A sparsity-based approach to rigid-body motion correction is presented for the first time in this paper. A motion is sought after such that the compensated MR image is maximally sparse/compressible among the infinite candidates. Iterative algorithms are proposed that jointly estimate the motion and the image content. The proposed method has a lot of merits, such as no need of additional data and loose requirement for the sampling sequence. Promising results are presented to demonstrate its performance.
@article{arxiv.1302.0077,
title = {Sparse MRI for motion correction},
author = {Zai Yang and Cishen Zhang and Lihua Xie},
journal= {arXiv preprint arXiv:1302.0077},
year = {2013}
}
Comments
To appear in Proceedings of ISBI 2013. 4 pages, 1 figure