Compressed Sensing MRI via Extended Anisotropic and Isotropic Total Variation
Compressed sensing (CS) is a technique to reconstruct images from undersampling data, reducing the scanning time of magnetic resonance imaging (MRI). It utilizes the sparsity of images in some transform domains. Total variation (TV) has been applied to enforce sparsity. However, traditional
TV based on the l
1-norm is not the most direct way to induce sparsity, and it cannot offer a sufficiently sparse representation. Since the l
p
-norm (0< p < 1) promotes the sparsity better than that of the l
1-norm, we
propose two extended TV algorithms based on the l
p
-norm: anisotropic and isotropic total p-variation (TpV). Then we introduce them to the MRI reconstruction model. We apply the Bregman iteration technique to handle the proposed optimization problem. During the
iteration, the p-shrinkage operator is employed to resolve the nonconvex problem caused by the l
p
-norm. Experimental results illustrate that our algorithms could offer the higher SNR and lower relative error compared with traditional TV algorithms and high-degree
TV (HDTV) algorithm in MRI reconstruction problem.
Keywords: COMPRESSED SENSING; EXTENDED TOTAL VARIATION; IMAGE RECONSTRUCTION; IP-NORM; MAGNETIC RESONANCE IMAGING
Document Type: Research Article
Publication date: 01 August 2019
- Journal of Medical Imaging and Health Informatics (JMIHI) is a medium to disseminate novel experimental and theoretical research results in the field of biomedicine, biology, clinical, rehabilitation engineering, medical image processing, bio-computing, D2H2, and other health related areas.
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