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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 i... |