Compressive sampling (CS) has been commonly employed in the field of magnetic resonance imaging (MRI) to accurately reconstruct sparse and compressive signals. In a MR image, a large amount of encoded information focuses on the origin of the k-space. For the 2D Cartesian K-space MRI, under-sampling the frequency-encoding (kx) dimension does not affect to the acquisition time, thus, only the phase-encoding (ky) dimension can be exploited. In the traditional random under-sampling approach, it acquired Gaussian random measurements along the phaseencoding (ky) in the k-space. In this paper, we proposed a hybrid under-sampling approach; the number of measurements in (ky) is divided into two portions: 70% of the measurements are for random under-sampling and 30% are for definite under-sampling near the origin of the k-space. The numerical simulation consequences pointed out that, in the lower region of the under-sampling ratio r, both the average error and the universal image quality index of the appointed scheme are drastically improved up to 55 and 77% respectively as compared to the traditional scheme. For the first time, instead of using highly computational complexity of many advanced reconstruction techniques, a simple and efficient CS method based simulation is proposed for MRI reconstruction improvement. These findings are very useful for designing new MRI data acquisition approaches for reducing the imaging time of current MRI systems.
Keywords: MRI; compressed sensing; k-space; non-linear conjugate gradient; power law.