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• W2328485015 startingPage "925" @default.
• W2328485015 abstract "The problem of compressive-sensing (CS) L2-L1-TV reconstruction of magnetic resonance (MR) scans from undersampled $k$-space data has been addressed in numerous studies. However, the regularization parameters in models of CS L2-L1-TV reconstruction are rarely studied. Once the regularization parameters are given, the solution for an MR reconstruction model is fixed and is less effective in the case of strong noise. To overcome this shortcoming, we present a new alternating formulation to replace the standard L2-L1-TV reconstruction model. A weighted-average alternating minimization method is proposed based on this new formulation and a convergence analysis of the method is carried out. The advantages of and the motivation for the proposed alternating formulation are explained. Experimental results demonstrate that the proposed formulation yields better reconstruction results in the case of strong noise and can improve image reconstruction via flexible parameter selection." @default.
• W2328485015 created "2016-06-24" @default.
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• W2328485015 date "2014-01-01" @default.
• W2328485015 modified "2023-09-25" @default.
• W2328485015 title "Weighted-average alternating minimization method for magnetic resonance image reconstruction based on compressive sensing" @default.
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• W2328485015 doi "https://doi.org/10.3934/ipi.2014.8.925" @default.
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