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Improving multi-channel compressed sensing MRI with reweighted l1 minimization

   
@article{QIMS3434,
	author = {Ching-Hua Chang and Jim X. Ji},
	title = {Improving multi-channel compressed sensing MRI with reweighted  l  1  minimization},
	journal = {Quantitative Imaging in Medicine and Surgery},
	volume = {4},
	number = {1},
	year = {2014},
	keywords = {},
	abstract = {Integrating compressed sensing (CS) and parallel imaging (PI) with multi-channel receiver has proven to be an effective technology to speed up magnetic resonance imaging (MRI). In this paper, we propose a method that extends the reweighted l1 minimization to the CS-MRI with multi-channel data. The method applies a reweighted l1 minimization algorithm to reconstruct each channel image, and then generates the final image by a sum-of-squares method. Computer simulations based on synthetic data and in vivo MRI imaging data show that the new method can improve the reconstruction quality at a slightly increased computation cost.},
	issn = {2223-4306},	url = {https://qims.amegroups.org/article/view/3434}
}