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Adaptive structured low rank algorithm for MR image recovery
Conference proceeding

Adaptive structured low rank algorithm for MR image recovery

Yue Hu, Xiaohan Liu and Mathews Jacob
2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Vol.2018-, pp.1260-1263
04/2018
DOI: 10.1109/ISBI.2018.8363800
PMID: 33623637
url
https://www.ncbi.nlm.nih.gov/pmc/articles/7897551View
Open Access

Abstract

We introduce an adaptive structured low rank algorithm to recover MR images from their undersampled Fourier coefficients. The image is modeled as a combination of a piece-wise constant component and a piecewise linear component. The Fourier coefficients of each component satisfy an annihilation relation, which results in a structured Toeplitz matrix. We exploit the low rank property of the matrices to formulate a combined regularized optimization problem, which can be solved efficiently. Numerical experiments indicate that the proposed algorithm provides improved recovery performance over the previously proposed algorithms.
Optimization TV Convolution structured low rank matrix Imaging Approximation algorithms MRI reconstruction Image reconstruction Compressed sensing

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