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Faster Convergence for CS-SENSE Reconstruction
Fast reconstruction is crucial for the implementation of CS-SENSE onclinical scanners. Thus, improvements of the reconstruction speed are desirable, both in terms of algorithms with improved convergence and parallel implementation. In this work, we propose a modified CS-SENSE reconstruction method based on the Nesterovs optimal gradientscheme, which is less sensitive to inaccuracies in the coil sensitivity estimation and has an improved convergence speed.
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CS-SENSE or Denoised SENSE: The Influence of Irregular Sampling in l1 Regularized SENSE Reconstruction
In this work, we investigate the influence of the sampling pattern on the convergence behaviour of {1-regularized SENSE reconstruction at different reduction factors. In other words, we try to answer the question what improvement can CS-SENSE provide over {1-denoised SENSE.
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Parameter-Free Compressed Sensing Reconstruction using Statistical Non-Local Self-Similarity Filtering
In this work, we present a CS reconstruction based on statistical non-local self-similarity filtering (STAINLeSS), in which the parameters are entirely determined by the noise estimation in the receive channels obtained from a standard noise measurement. The method achieves improved image quality compared to wavelet based CS reconstruction in particular in SENSE based multi-coil reconstruction due to itsadaptivity to spatially varying noise. The proposed method providesimproved robustness due to the lack of free parameters which is crucial for the clinical applicability of CS.
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Search results also available in MS Excel format.