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Filter-based compressed sensing MRI reconstruction
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DOI：10.1002/ima.22171.png)
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Abstract
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Compressed sensing (CS) enables to reconstruct MR images from highly undersampled k-space data by exploiting the sparsity which is implicit in the images. In this article, an MR image as a combination of a high-frequency component HP and a low-frequency component LP through a pair of filters has been proposed to express. Since HP exhibits a sparser representation in the wavelet transform domain, reconstructing HP and LP separately yields a better result than reconstructing directly. Two parameters, normalized sparsity (NS) and power ratio (PR), are defined to design the filters, that is, the high-pass filter H-HP and the low-pass filter H-LP. H-HP is applied to pick out high-frequency k-space data for the reconstruction of high-frequency image HP; while H-LP is used for filtering , which is reconstructed from the entire undersampled k-space data to obtain the low-frequency reconstruction LP. Summing HP and LP leads to the final reconstruction of . Experimental results demonstrate that the proposed method outperforms the conventional CS-MRI method. It provides 2-4 dB improvement in peak signal to noise ratio (PSNR) value and preserves more edges and details in the images. (c) 2016 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 26, 173-178, 2016
Keywords:
compressed sensing (CS)
magnetic resonance imaging (MRI)
high-pass filter
sparsity
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