Low-Rank and Sparse Matrix Decomposition with a-priori knowledge for Dynamic 3D MRI reconstruction
Computer Vision and Pattern Recognition
2014-11-25 v1
Abstract
It has been recently shown that incorporating priori knowledge significantly improves the performance of basic compressive sensing based approaches. We have managed to successfully exploit this idea for recovering a matrix as a summation of a Low-rank and a Sparse component from compressive measurements. When applied to the problem of construction of 4D Cardiac MR image sequences in real-time from highly under-sampled space data, our proposed method achieves superior reconstruction quality compared to the other state-of-the-art methods.
Keywords
Cite
@article{arxiv.1411.6206,
title = {Low-Rank and Sparse Matrix Decomposition with a-priori knowledge for Dynamic 3D MRI reconstruction},
author = {Dornoosh Zonoobi and Shahrooz Faghih Roohi and Ashraf A. Kassim},
journal= {arXiv preprint arXiv:1411.6206},
year = {2014}
}
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