Hybrid Random Under-Sampling Approach in MRI Compressed Sensing

Abstract

Compressive sampling has been commonly employed in the field of magnetic resonance imaging (MRI) to accurately reconstruct sparse and compressive signals. In MRI acquisition, a large amount of encoded information focuses on the origin of the k-space. With a fixed compression ratio: (a) in the traditional random under-sampling approach, the sampled horizontal lines in the binary mask are completely based on the power law; (b) in the proposed hybrid random under-sampling approach, the total number of sampled horizontal lines is divided into two parts, the large first part is still based on the power law, while the small other part is enhanced with the remaining lines which are near the origin of k-space. Because the amount of encoded information is concentrated at the origin of the k-space, the proposed method suggests that the amount of useful information will be collected more and therefore the MRI image recovery will be more accurate. The numerical simulation consequences pointed out that the average error of the appointed scheme decreased by 17.78%, compared to the traditional scheme.