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• W2778133899 abstract "Low sampling density and reduced signal-to-noise ratio resulting from accelerated magnetic resonance data acquisition calls for sparsity promoting regularization for improved reconstruction. The simplest optimization strategy relies on thresholded Landweber algorithm that results in reconstructions with slow rate of convergence. A varying threshold can accelerate the rate of convergence, but does not guarantee low steady-state errors. In this paper, we propose a statistical approach to optimize the tradeoff between steady-state reconstruction error and speed of convergence, by iteratively adapting the regularization parameter. Analogous to the generalized discrepancy principle that implicates the nonnegativity of discrepancy level (absolute difference between l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> -norms of consistency and perturbation errors) as provided by regularized reconstruction, the proposed method of adaptation entails reduction of the discrepancy level in l <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> -norm of the consistency and sparse approximation errors. In discrete form, this is deduced from the convergence properties of Landweber updates soft thresholded using monotonically decreasing thresholds. With very large number of iterations, the thresholds approach a lower bound determined by the noise level and adaptation function. Further improvements in image quality can be achieved by separately adapting the thresholds in each wavelet subband. Application of adaptive regularization combined with over-relaxation to parallel MRI shows significant improvements in both speed and image quality." @default.
• W2778133899 created "2018-01-05" @default.
• W2778133899 creator A5015460041 @default.
• W2778133899 creator A5033569193 @default.
• W2778133899 date "2018-03-01" @default.
• W2778133899 modified "2023-10-12" @default.
• W2778133899 title "Sparsity Promoting Adaptive Regularization for Compressed Sensing Parallel MRI" @default.
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• W2778133899 doi "https://doi.org/10.1109/tci.2017.2787911" @default.
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