TY - GEN
T1 - Efficient resolution enhancement algorithm for compressive sensing magnetic resonance image reconstruction
AU - Omer, Osama A.
AU - Bassiouny, M. Atef
AU - Morooka, Ken’ichi
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Magnetic resonance imaging (MRI) has been widely applied in a number of clinical and preclinical applications. However, the resolution of the reconstructed images using conventional algorithms are often insufficient to distinguish diagnostically crucial information due to limited measurements. In this paper, we consider the problem of reconstructing a high resolution (HR) MRI signal from very limited measurements. The proposed algorithm is based on compressed sensing, which combines wavelet sparsity with the sparsity of image gradients, where the magnetic resonance (MR) images are generally sparse in wavelet and gradient domain. The main goal of the proposed algorithm is to reconstruct the HR MR image directly from a few measurements. Unlike the compressed sensing (CS) MRI reconstruction algorithms, the proposed algorithm uses multi measurements to reconstruct HR image. Also, unlike the resolution enhancement algorithms, the proposed algorithm perform resolution enhancement of MR image simultaneously with the reconstruction process from few measurements. The proposed algorithm is compared with three state-of-the-art CS-MRI reconstruction algorithms in sense of signal-tonoise ratio and full-with-half-maximum values.
AB - Magnetic resonance imaging (MRI) has been widely applied in a number of clinical and preclinical applications. However, the resolution of the reconstructed images using conventional algorithms are often insufficient to distinguish diagnostically crucial information due to limited measurements. In this paper, we consider the problem of reconstructing a high resolution (HR) MRI signal from very limited measurements. The proposed algorithm is based on compressed sensing, which combines wavelet sparsity with the sparsity of image gradients, where the magnetic resonance (MR) images are generally sparse in wavelet and gradient domain. The main goal of the proposed algorithm is to reconstruct the HR MR image directly from a few measurements. Unlike the compressed sensing (CS) MRI reconstruction algorithms, the proposed algorithm uses multi measurements to reconstruct HR image. Also, unlike the resolution enhancement algorithms, the proposed algorithm perform resolution enhancement of MR image simultaneously with the reconstruction process from few measurements. The proposed algorithm is compared with three state-of-the-art CS-MRI reconstruction algorithms in sense of signal-tonoise ratio and full-with-half-maximum values.
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U2 - 10.1007/978-3-319-23231-7_46
DO - 10.1007/978-3-319-23231-7_46
M3 - Conference contribution
AN - SCOPUS:84944754482
SN - 9783319232300
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 519
EP - 527
BT - Image Analysis and Processing – ICIAP 2015 - 18th International Conference, Proceedings
A2 - Murino, Vittorio
A2 - Puppo, Enrico
A2 - Murino, Vittorio
PB - Springer Verlag
T2 - 18th International Conference on Image Analysis and Processing, ICIAP 2015
Y2 - 7 September 2015 through 11 September 2015
ER -