Comparative Evaluation of l1 vs lp Minimization Techniques for Compressed Sensing MRI
نویسندگان
چکیده
Introduction Compressed sensing (CS) has been shown to provide accurate reconstructions from highly undersampled data for certain types of MR acquisitions [1, 2]. This offers the promise of faster MR acquisitions, and further speed gains are possible when CS is used in conjunction with parallel acquisition schemes such as SENSE [3]. Several approaches have been recently proposed to reconstruct images from even fewer measurements than those required by standard l1-norm compressed sensing [2,4,5]. The purpose of this study was to test and compare standard l1norm CS with two such approaches based on the lp quasi-norm [2,4,6] across different sampling pattern densities and parameterizations.
منابع مشابه
A Hybrid L0-L1 Minimization Algorithm for Compressed Sensing MRI
INTRODUCTION Both L1 minimization [1] and homotopic L0 minimization [2] techniques have shown success in compressed-sensing MRI reconstruction using reduced k-space data. L1 minimization algorithm is known to usually shrink the magnitude of reconstructions especially for larger coefficients [1, 3] and non-convex penalty used in homotopic L0 minimization is advocated to replace L1 penalty [3]. H...
متن کاملA Hybrid L0-L1 Minimization Algorithm for Compressed Sensing MRI
INTRODUCTION Both L1 minimization [1] and homotopic L0 minimization [2] techniques have shown success in compressed-sensing MRI reconstruction using reduced k-space data. L1 minimization algorithm is known to usually shrink the magnitude of reconstructions especially for larger coefficients [1, 3] and non-convex penalty used in homotopic L0 minimization is advocated to replace L1 penalty [3]. H...
متن کاملImproved k-t FOCUSS using a sparse Bayesian learning
Introduction: In dynamic MRI, spatio-temporal resolution is a very important issue. Recently, compressed sensing approach has become a highly attracted imaging technique since it enables accelerated acquisition without aliasing artifacts. Our group has proposed an l1-norm based compressed sensing dynamic MRI called k-t FOCUSS which outperforms the existing methods. However, it is known that the...
متن کاملCompressed Sensing with Transform Domain Dependencies for Coronary MRI
Fig 3: Comparison of BLS-GSM CS and l1 norm CS for imaging of right coronary artery. Fig. 1: a) Wavelet coefficients of a 2D slice of a coronary image. b) Random permutation of the same coefficients shown in (a). Both data have equivalent lp norm, which suggests CS lp norm regularizers do not take into account the clustering and correlation of information in the transform domain. Compressed Sen...
متن کاملA comparison of typical ℓp minimization algorithms
Recently, compressed sensing has been widely applied to various areas such as signal processing, machine learning, and pattern recognition. To find the sparse representation of a vector w.r.t. a dictionary, an l1 minimization problem, which is convex, is usually solved in order to overcome the computational difficulty. However, to guarantee that the l1 minimizer is close to the sparsest solutio...
متن کامل