Magnetic Resonance Imaging (MRI) is a noninvasive imaging method widely used in medical diagnosis. Data in MRI are obtained line-by-line within the K-space, where there are usually a great number of such lines. For this reason, magnetic resonance imaging is slow. MRI ca
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Magnetic Resonance Imaging (MRI) is a noninvasive imaging method widely used in medical diagnosis. Data in MRI are obtained line-by-line within the K-space, where there are usually a great number of such lines. For this reason, magnetic resonance imaging is slow. MRI can be accelerated through several methods such as parallel imaging and compressed sensing, where a fraction of the K-space lines is obtained. According to the advanced mathematical theories about compressed sensing, images entailing sparse representation within a certain area can be restored through a random sub sampling of K-space data. MRI images are often sparse in an appropriate conversion range, where imaging speed can be significantly improved through the compressed sensing theory. The complete random sub sampling of K-space creates an extremely high degree of incoherent artifacts for simplifying the mathematical calculations. Random sampling of K-space points is generally impractical in all dimensions, because the K-space paths will be smooth only when hardware and physiological considerations have been met. Our goal is to design practical decoherence sub sampling models simulating the interference properties of the pure random sub sampling until it is possible to quickly gather information. This paper introduces 3 sub sampling techniques for K-space data, providing the best efficiency in the production of sparse incoherent artifacts based on the compressed sensing theory. All the proposed methods were simulated on real-life data compared against the MRI results.
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